IEEE/CAA Journal of Automatica Sinica

Multi-UAV Cooperative Pursuit Strategy With Limited Visual Field in Urban Airspace: A Multi-Agent Reinforcement Learning Approach

Zhe Peng, Guohua Wu, Biao Luo, Ling Wang

, Available online , doi: 10.1109/JAS.2024.124965

[Abstract](88) [FullText HTML] (0) [PDF 8160KB](25)

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The application of multiple unmanned aerial vehicles (UAVs) for the pursuit and capture of unauthorized UAVs has emerged as a novel approach to ensuring the safety of urban airspace. However, pursuit UAVs necessitate the utilization of their own sensors to proactively gather information from the unauthorized UAV. Considering the restricted sensing range of sensors, this paper proposes a multi-UAV with limited visual field pursuit-evasion (MUV-PE) problem. Each pursuer has a visual field characterized by limited perception distance and viewing angle, potentially obstructed by buildings. Only when the unauthorized UAV, i.e., the evader, enters the visual field of any pursuer can its position be acquired. The objective of the pursuers is to capture the evader as soon as possible without collision. To address this problem, we propose the normalizing flow actor with graph attention critic (NAGC) algorithm, a multi-agent reinforcement learning (MARL) approach. NAGC executes normalizing flows to augment the flexibility of policy network, enabling the agent to sample actions from more intricate distributions rather than common distributions. To enhance the capability of simultaneously comprehending spatial relationships among multiple UAVs and environmental obstacles, NAGC integrates the “obstacle-target” graph attention networks, significantly aiding pursuers in supporting search or pursuit activities. Extensive experiments conducted in a high-precision simulator validate the promising performance of the NAGC algorithm.

A Transactional-Behavior-Based Hierarchical Gated Network for Credit Card Fraud Detection

Yu Xie, MengChu Zhou, Guanjun Liu, Lifei Wei, Honghao Zhu, Pasquale De Meo

, Available online , doi: 10.1109/JAS.2025.125243

[Abstract](124) [FullText HTML] (0) [PDF 10002KB](23)

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The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users’ historical and current transactional behaviour. Such information is crucial for revealing users’ sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art.

Online Estimation of DC-link Capacitor Parameters of Three-Level NPC Converters Using Inherent Signals Analysis

Ricardo Lucio de Araujo Ribeiro, Reuben Palmer Rezende de Sousa, Alexandre Cunha Oliveira, Antonio Marcus Nogueira Lima, Qing-Long Han

, Available online , doi: 10.1109/JAS.2025.125159

[Abstract](131) [FullText HTML] (2) [PDF 2025KB](19)

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This paper presents a method for estimating the parameters of DC-link capacitors in three-level NPC voltage source inverters (3L-NPC-VSI) used in grid-tied systems. The technique uses the signals generated by the intermodulation caused by the PWM strategy and converter topology interaction to estimate the capacitor parameters of the converter DC-link. It utilizes an observer-based structure consisting of a recursive noninteger sliding discrete Fourier transform (rnSDFT) and an RLS filter improved with a forgetting factor (oSDFT-RLS) to accurately estimate the capacitance and equivalent series resistance (ESR). Importantly, this method does not require additional sensors beyond those already installed in off-the-shelf 3L-NPC-VSI systems, ensuring its noninvasiveness. Furthermore, the oSDFT-RLS estimates capacitor parameters in the time-frequency domain, enabling the tracking of capacitor degradation and predicting potential faults. Experimental results from the laboratory setup demonstrate the effectiveness of the proposed condition monitoring method.

Nonlinear Integral-Ameliorated Model for Dynamic Convex Optimization With Perturbance Considered

Kangze Zheng, Yunong Zhang

, Available online , doi: 10.1109/JAS.2024.124788

[Abstract](93) [FullText HTML] (0) [PDF 9025KB](9)

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This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints. They pose a challenge as their optimal solutions evolve with time. Traditional iteration-based methods that exactly solve the problem at each time instant, fail to precisely and real-time track the solution due to computational and communication bottlenecks. Our model, through rigorous theoretical analyses, is able to reduce the optimality gap (i.e., the difference between the model state and optimal solution) to zero in a finite time, and thus, track the solution online. Besides, perturbance is taken into account. We prove that under certain conditions, our model can totally tolerate an important kind of noise that we call “error-related noise”. In numerical experiments, compared with six existing methods, our model exhibits superior robustness when contaminated by the error-related noise. The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information, and introducing an integral term as well as the class $ {{{\mathrm{C}}}^0_\text{L}} $ functions to enhance convergence and noise resistance. Finally, we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations. The results indicate that our model achieves the best performance among various models employed within the framework.

A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority

Liang Xin, Zhi-Qiang Long

, Available online , doi: 10.1109/JAS.2024.124683

[Abstract](212) [FullText HTML] (0) [PDF 2780KB](43)

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Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.

A Hierarchical Stochastic Network Approach for Fault Diagnosis of Complex Industrial Processes

Mingjie Lv, Yonggang Li, Huanzhi Gao, Bei Sun, Keke Huang, Chunhua Yang, Weihua Gui

, Available online , doi: 10.1109/JAS.2025.125249

[Abstract](61) [FullText HTML] (0) [PDF 39983KB](11)

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Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions. This poses three challenges for precise fault diagnosis, including random noise interference, less distinguishability between multi-class faults, and the new fault emerging. To address these issues, this study formulates fault diagnosis in uncertain industrial processes as a multi-level refined fault diagnosis problem. A hierarchical stochastic network approach is proposed to refine fault diagnosis of multi-class faults. This method considers the augmentation of fault categories as naturally following a hierarchical structure. At each hierarchical stage, stochastic network methods are designed according to the sources of uncertainty. For fault feature extraction, a doubly stochastic attention-based variational graph autoencoder is introduced to suppress noise during the message-passing process, ensuring the extraction of high-quality fault features and providing the provision of differentiated information. Subsequently, multiple stochastic configuration networks are deployed to realize multi-level fault diagnosis from coarse to fine granularity via a hierarchical structure rather than treating all faults equally. This approach effectively enhances the precision of multi-class fault diagnosis and ensures its robust generalization capability. Finally, the feasibility and effectiveness of the proposed method are validated using two industrial processes. The results demonstrate that the proposed method can effectively suppress the random noise interference and adapt to the emergence of small samples and imbalanced extreme fault-type data, achieving a satisfactory fault diagnosis performance.

Deep Reinforcement Learning for Zero-Shot Coverage Path Planning With Mobile Robots

José Pedro Carvalho, A. Pedro Aguiar

, Available online , doi: 10.1109/JAS.2024.125064

[Abstract](401) [FullText HTML] (0) [PDF 1694KB](108)

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The ability of mobile robots to plan and execute a path is foundational to various path-planning challenges, particularly Coverage Path Planning. While this task has been typically tackled with classical algorithms, these often struggle with flexibility and adaptability in unknown environments. On the other hand, recent advances in Reinforcement Learning offer promising approaches, yet a significant gap in the literature remains when it comes to generalization over a large number of parameters. This paper presents a unified, generalized framework for coverage path planning that leverages value-based deep reinforcement learning techniques. The novelty of the framework comes from the design of an observation space that accommodates different map sizes, an action masking scheme that guarantees safety and robustness while also serving as a learning-from-demonstration technique during training, and a unique reward function that yields value functions that are size-invariant. These are coupled with a curriculum learning-based training strategy and parametric environment randomization, enabling the agent to tackle complete or partial coverage path planning with perfect or incomplete knowledge while generalizing to different map sizes, configurations, sensor payloads, and sub-tasks. Our empirical results show that the algorithm can perform zero-shot learning scenarios at a near-optimal level in environments that follow a similar distribution as during training, outperforming a greedy heuristic by sixfold. Furthermore, in out-of-distribution environments, our method surpasses existing state-of-the-art algorithms in most zero-shot and all few-shot scenarios, paving the way for generalizable and adaptable path-planning algorithms.

Parallel Experiments: From The Human Participated to A Virtual-Real Hybrid Paradigm

Peijun Ye, Xiao Xue, Qinghua Ni, Jing Yang, Fei-Yue Wang

, Available online , doi: 10.1109/JAS.2025.125474

[Abstract](11) [FullText HTML] (0) [PDF 4438KB](1)

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LTDNet: A Lightweight Text Detector for Real-Time Arbitrary-Shape Traffic Text Detection

Runmin Wang, Yanbin Zhu, Ziyu Zhu, Lingxin Cui, Zukun Wan, Anna Zhu, Yajun Ding, Shengyou Qian, Changxin Gao, Nong Sang

, Available online , doi: 10.1109/JAS.2024.125022

[Abstract](167) [FullText HTML] (0) [PDF 14173KB](40)

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Traffic text detection plays a vital role in understanding traffic scenes. Traffic text, a distinct subset of natural scene text, faces specific challenges not found in natural scene text detection, including false alarms from non-traffic text sources, such as roadside advertisements and building signs. Existing state-of-the-art methods employ increasingly complex detection frameworks to pursue higher accuracy, leading to challenges with real-time performance. In response to this issue, we propose a real-time and efficient traffic text detector named LTDNet, which strikes a balance between accuracy and real-time capabilities. LTDNet integrates three essential techniques to address these challenges effectively. First, a cascaded multilevel feature fusion network is employed to mitigate the limitations of lightweight backbone networks, thereby enhancing detection accuracy. Second, a lightweight feature attention module is introduced to enhance inference speed without compromising accuracy. Finally, a novel point-to-edge distance vector loss function is proposed to precisely localize text instance boundaries within traffic contexts. The superiority of our method is validated through extensive experiments on five publicly available datasets, demonstrating its state-of-the-art performance. The code will be released at https://github.com/runminwang/LTDNet.

Hybrid Event-Triggered Control With Stability Analysis

Ding Wang, Lingzhi Hu, Junfei Qiao

, Available online , doi: 10.1109/JAS.2024.125067

[Abstract](24) [FullText HTML] (0) [PDF 1156KB](13)

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In this paper, a novel hybrid event-triggered control (ETC) method is developed based on the online action-critic technique, which aims at tackling the optimal regulation problem of discrete-time nonlinear systems. In order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain the initial admissible control policy by using an offline iterative method under the time-triggered control framework. Subsequently, a general triggering condition is designed based on the uniform ultimate boundedness of the controlled system. In order to determine a constant interval which can ensure the system stability, another triggering condition is introduced and the asymptotic stability of the closed-loop system satisfying this condition is analyzed from the perspective of the input-to-state stability. The designed online hybrid ETC method not only further improves control efficiency, but also avoids the continuous judgment of the corresponding triggering condition. In addition, the event-based control law can approach the optimal control input within a finite approximation error. Finally, two experimental examples with physical background are conducted to indicate the present results.

Average Consensus of Whole-Process Privacy Preservation

Lianghao Ji, Shaohong Tang, Xing Guo, Yan Xie

, Available online , doi: 10.1109/JAS.2024.124731

[Abstract](7) [FullText HTML] (0) [PDF 475KB](4)

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Distributed Nash Equilibrium Seeking for Games Under Unknown Dead-Zone Inputs and DoS Attacks: A Digital Twin Approach

Ming Yang, Maojiao Ye, Jun Shi

, Available online , doi: 10.1109/JAS.2024.124875

[Abstract](15) [FullText HTML] (0) [PDF 899KB](8)

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System Identification in the Network Era: A Survey of Data Issues and Innovative Approaches

Qing-Guo Wang, Liang Zhang

, Available online , doi: 10.1109/JAS.2024.125109

[Abstract](37) [FullText HTML] (0) [PDF 1224KB](12)

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System identification is a data-driven modeling technique that originates from the control field. It constructs models from data to mimic the behavior of dynamic systems. However, in the network era, scenarios such as sensor malfunctions, packet loss, cyber-attacks, and big data affect the quality, integrity, and security of the data. These data issues pose significant challenges to traditional system identification methods. This paper presents a comprehensive survey of the emergent challenges and advances in system identification in the network era. It explores cutting-edge methodologies to address data issues such as data loss, outliers, noise and nonlinear system identification for complex systems. To tackle the data loss, the methods based on imputation and likelihood-based inference (e.g., expectation maximization) have been employed. For outliers and noise, methods like robust regression (e.g., least median of squares, least trimmed squares) and low-rank matrix decomposition show progress in maintaining data integrity. Nonlinear system identification has advanced through kernel-based methods and neural networks, which can model complex data patterns. Finally, this paper provides valuable insights into potential directions for future research.

An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape

Jicheng Zhu, Zhaohui Jiang, Dong Pan, Haoyang Yu, Chuan Xu, Ke Zhou, Weihua Gui

, Available online , doi: 10.1109/JAS.2025.125192

[Abstract](162) [FullText HTML] (0) [PDF 4115KB](29)

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Today, a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace (BF). Previous research on charging operations always focused on the two-dimensional shape of the burden surface (i.e., a single radial profile) while neglecting the unique feature of global dissymmetry, severely restricting the development of precise charging. For this reason, this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface, which is the first attempt in this field. First, a practicable region partitioning scheme is introduced, and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model. Next, the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation, which forms the basis for defining a novel operational parameter with variable-speed utility, referred to as the neotype charging matrix (NCM). To find the best NCM, a customized NCM optimization strategy, involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm, is further developed. The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description. Moreover, the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix (CM).

A Robust Large-Scale Multiagent Deep Reinforcement Learning Method for Coordinated Automatic Generation Control of Integrated Energy Systems in a Performance-Based Frequency Regulation Market

Jiawen Li, Tao Zhou

, Available online , doi: 10.1109/JAS.2024.124482

[Abstract](49) [FullText HTML] (0) [PDF 17838KB](19)

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To enhance the frequency stability and lower the regulation mileage payment of a multiarea integrated energy system (IES) that supports the power Internet of Things (IoT), this paper proposes a data-driven cooperative method for automatic generation control (AGC). The method consists of adaptive fractional-order proportional-integral (FOPI) controllers and a novel efficient integration exploration multiagent twin delayed deep deterministic policy gradient (EIE-MATD3) algorithm. The FOPI controllers are designed for each area based on the performance-based frequency regulation market mechanism. The EIE-MATD3 algorithm is used to tune the coefficients of the FOPI controllers in real time using centralized training and decentralized execution. The algorithm incorporates imitation learning and efficient integration exploration to obtain a more robust coordinated control strategy. An experiment on the four-area China Southern Grid (CSG) real-time digital system shows that the proposed method can improve the control performance and reduce the regulation mileage payment of each area in the IES.

Distributed Saturated Impulsive Quasi-Consensus for Leader-Follower Multi-Agent Systems: An Open Topology Framework

Haitao Zhu, Jianquan Lu, Yijun Lou, Xinsong Yang

, Available online , doi: 10.1109/JAS.2024.124743

[Abstract](10) [FullText HTML] (0) [PDF 1126KB](9)

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Switching-Like Sliding Mode Security Control Against DoS Attacks: A Novel Attack-Related Adaptive Event-Triggered Scheme

Jiancun Wu, Zhiru Cao, Engang Tian, Chen Peng

, Available online , doi: 10.1109/JAS.2025.125189

[Abstract](23) [FullText HTML] (0) [PDF 2262KB](11)

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In this paper, a security defense issue is investigated for networked control systems susceptible to stochastic Denial of Service (DoS) attacks by using the sliding mode control method. To utilize network communication resources more effectively, a novel adaptive event-triggered (AET) mechanism is introduced, whose triggering coefficient can be adaptively adjusted according to the evolution trend of system states. Differing from existing ET mechanisms, the proposed one demonstrates exceptional relevance and flexibility. It is closely related to attack probability, and its triggering coefficient dynamically adjusts depending on the presence or absence of an attack. To leverage attacker information more effectively, a switching-like sliding mode security controller is designed, which can autonomously select different controller gains based on the sliding function representing the attack situation. Sufficient conditions for the existence of the switching-like sliding mode secure controller are presented to ensure the stochastic stability of the system and the reachability of the sliding surface. Compared with existing time-invariant control strategies within the triggered interval, more resilient defense performance can be expected since the correlation with attack information is established in both the proposed AET scheme and the control strategy. Finally, a simulation example is conducted to verify the effectiveness and feasibility of the proposed security control method.

Unsupervised Dynamic Discrete Structure Learning: A Geometric Evolution Method

Chaoqun Fei, Yangyang Li, Xikun Huang, Ge Zhang, Ruqian Lu

, Available online , doi: 10.1109/JAS.2025.125165

[Abstract](14) [FullText HTML] (0) [PDF 1488KB](3)

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Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical method for discovering latent geometric structures, has provided important nonlinear insight for the theoretical development of unsupervised representation learning. However, due to the shallow learning mechanism of the existing methods, they can only exploit the simple geometric structure embedded in the initial data, such as the local linear structure. Traditional manifold learning methods are fairly limited in mining higher-order nonlinear geometric information, which is also crucial for the development of unsupervised representation learning. To address the abovementioned limitations, this paper proposes a novel dynamic geometric structure learning model (DGSL) to explore the true latent nonlinear geometric structure. Specifically, by mathematically analysing the reconstruction loss function of manifold learning, we first provide universal geometric relational function between the curvature and the non-Euclidean metric of the initial data. Then, we leverage geometric flow to design a deeply iterative learning model to optimize this relational function. Our method can be viewed as a general-purpose algorithm for mining latent geometric structures, which can enhance the performance of geometric representation methods. Experimentally, we perform a set of representation learning tasks on several datasets. The experimental results show that our proposed method is superior to traditional methods.

Adaptive Data-Driven Coordinated Control of UUVs for Maritime Search and Rescue

Hao-Liang Wang, De-Zhi Yu, Li-Yu Lu, Zhou-Hua Peng

, Available online , doi: 10.1109/JAS.2024.124767

[Abstract](10) [FullText HTML] (0) [PDF 1082KB](4)

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A Further Study on Terminal Sliding Mode Control for Nonlinear Systems

Zhe Sun, Zhipeng Li, Bo Chen, Yuan Zhou, Jinchuan Zheng, Zhihong Man

, Available online , doi: 10.1109/JAS.2025.125240

[Abstract](99) [FullText HTML] (0) [PDF 6692KB](37)

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In this paper, a unified terminal sliding mode (UTSM) control method is proposed for second-order nonlinear systems with uncertainties and disturbances. It is seen that the newly defined terminal sliding surface is integrated with both conventional and fast terminal sliding mode and exhibits design advantages such as a variable exponent, adjustable sliding mode parameters, and chattering-alleviation effect. The inherent dynamic properties of the closed-loop systems with the UTSM control are discussed in detail via the phase plane and Lyapunov stability theory. Both numerical simulations and experimental results show the flexible sliding manifold design, strong robustness against uncertain dynamics, and effective attenuation of chattering phenomenon.

Robot Impedance Iterative Learning With Sparse Online Gaussian Process

Yongping Pan, Tian Shi, Wei Li, Bin Xu, Choon Ki Ahn

, Available online , doi: 10.1109/JAS.2025.125195

[Abstract](24) [FullText HTML] (0) [PDF 1495KB](12)

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Robot interaction control with variable impedance parameters may conform to task requirements during continuous interaction with dynamic environments. Iterative learning (IL) is effective to learn desired impedance parameters for robots under unknown environments, and Gaussian process (GP) is a nonparametric Bayesian approach that models complicated functions with provable confidence using limited data. In this paper, we propose an impedance IL method enhanced by a sparse online Gaussian process (SOGP) to speed up learning convergence and improve generalization. The SOGP for variable impedance modeling is updated in the same iteration by removing similar data points from previous iterations while learning impedance parameters in multiple iterations. The proposed IL-SOGP method is verified by high-fidelity simulations of a collaborative robot with 7 degrees of freedom based on the admittance control framework. It is shown that the proposed method accelerates iterative convergence and improves generalization compared to the classical IL-based impedance learning method.

Accelerated Distributed Cooperative Energy Management for Integrated Energy Systems

Lining Liu, Yulong Huang, Chao Deng

, Available online , doi: 10.1109/JAS.2025.125489

[Abstract](77) [FullText HTML] (0) [PDF 3725KB](24)

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This paper is concerned with the problem of distributed coordination energy management of integrated energy systems (IESs). First, an energy management model for IESs is established and formulated as a distributed constrained optimization problem. Then, an accelerated distributed event-triggered algorithm is developed to solve the problem. Compared with the existing algorithms, the developed algorithm simultaneously offers two advantages. On the one hand, the convergence speed of the algorithm is improved greatly by incorporating the second-order information. On the other hand, the algorithm is implemented with asynchronous communication by an event-triggered mechanism, effectively reducing communication interact. Furthermore, the convergence and optimality of the algorithm are analyzed rigorously based on Lyapunov method. Finally, simulation studies are provided to validate the effectiveness of the algorithm.

Fixed-Time Stability of Random Nonlinear Systems

Fuyong Wang, Jiayi Gong, Zhongxin Liu, Fei Chen

, Available online , doi: 10.1109/JAS.2024.124353

[Abstract](128) [FullText HTML] (0) [PDF 471KB](26)

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Extended Dissipative Observer-Based Plug-and-Play Control for Large-Scale Interconnected Systems

Xiaohui Hu, Chen Peng, Hao Shen, Engang Tian

, Available online , doi: 10.1109/JAS.2025.125360

[Abstract](21) [FullText HTML] (0) [PDF 2688KB](11)

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In this study, a novel observer-based scalable control scheme for large-scale systems (LSSs) with several interconnected subsystems is explored. Firstly, a scalable observer-based controller is designed to address complex situations where system states are difficult to measure directly. Secondly, unlike the limited cascade and ring topology connections in previous results, this study considers a universal arbitrary topology. Furthermore, it is noteworthy that the plug-and-play (PnP) capability of LSSs is guaranteed thanks to the proposed scalable scheme. Specifically, when subsystems are added or removed, only the controller gains of directly connected neighbors need updating, eliminating the need to redesign the entire system. Moreover, by choosing a Lyapunov-Krasovskii function with a quadratic matrix-valued polynomial, sufficient conditions are deduced to guarantee the global exponential stability with the desired extended dissipative performance for the resulted LSSs. Finally, the effectiveness of the employed scheme is verified by numerical and microgrid examples.

Pattern Optimization of Fractional Diffusive Schnakenberg System by PD Control Strategy

Shunke Dong, Min Xiao, Zhengxin Wang, Wenwu Yu, Weixing Zheng, Leszek Rutkowski

, Available online , doi: 10.1109/JAS.2025.125303

[Abstract](10) [FullText HTML] (0) [PDF 29205KB](3)

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Reaction-diffusion systems are widely used to describe pattern formation, and various control strategies have been applied to reaction-diffusion systems to achieve control objectives such as boundary control, output feedback stabilization, and synchronization. However, controlling pattern dynamics in reaction-diffusion systems with fractional-order diffusion remains an unresolved problem. This paper presents a proportional-derivative (PD) control strategy for the Schnakenberg system with fractional-order diffusion and cross-diffusion. Theoretical analysis explores the amplitude equation near the Turing bifurcation threshold, determining the selection and stability of pattern formations. Numerical simulations demonstrate that the PD controller accomplishes the modification of pattern structures and suppression of Turing instability by adjusting only two control parameters. Additionally, it is found that for smaller fractional diffusion order, the region can accommodate more hexagonal and stripe patterns in space. This work contributes to the control of complex pattern dynamics and offers a new approach to enhancing stability in fractional reaction-diffusion systems.

A New Parameter Estimation Methodology Using Steady State Yaw Rate Measurements for Lateral Vehicle Dynamics

Zhihong Man, Mingcong Deng, Zenghui Wang, Qing-long Han

, Available online , doi: 10.1109/JAS.2025.125366

[Abstract](19) [FullText HTML] (0) [PDF 5749KB](6)

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In this paper, the lateral dynamics of road vehicles (LDRV) is further studied from the viewpoint of vehicle informatics. It is seen that LDRV is first decoupled and the vehicle slip angle is proved to be observable from the yaw rate measurements. A new methodology of parameter estimation using steady-state yaw rate measurements (PESYRM) is then developed to accurately estimate the parameters of LDRV. The important characteristics of PESYRM comprise four parts: ( i ) The steering angle input to LDRV is chosen as the linear combination of sinusoids; ( ii ) Only the steady state information of yaw rate in any fundamental period is required to accurately estimate the unknown parameters of LDRV; ( iii ) Unlike many existing parameter estimation methods, the time consuming computing of the inverse of high-dimensional data matrix is avoided by making full use of the orthogonal properties of trigonometric base functions; ( iv ) All of system information of LDRV is embedded in the measurements of the steady state yaw rate in any fundamental period. A simulation example is carried out to show the advantages and effectiveness of the new research findings for LDRV.

Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

Qiong Li, Xiaotong Liu, Xuecai Hu, Md Atiqur Rahman Ahad, Min Ren, Li Yao, Yongzhen Huang

, Available online

[Abstract](9) [FullText HTML] (0) [PDF 2642KB](4)

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Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.

Synthesis of Optimal Stealthy Attacks Against Diagnosability in Labeled Petri Nets

Ruotian Liu, Agostino Marcello Mangini, Maria Pia Fanti

, Available online

[Abstract](24) [FullText HTML] (0) [PDF 1403KB](9)

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This paper addresses the diagnosability analysis problem under external malicious attacks of a networked discrete event system modeled by labeled Petri net. In particular, we focus on a stealthy replacement attack to alter or corrupt the observation of the system, in which the transition labels are replaced by others or empty string, and its attack stealthiness requires that the corrupted observations should be contained in the behavior of system. The aim of this work is, from an attacker viewpoint, to design a stealthy replacement attack for violating the diagnosability of system. To this end, we first build a new structure, called complete unfolded verifier, with the notion of a predefined elementary unsound path that leads to the violation of diagnosability, which is used to enumerate all the potential attacked paths to be transformed into elementary unsound ones. Then an optimal attack synthesis problem in terms of minimum energy cost is formulated by determining whether an elementary unsound path is generated via solving a set of integer nonlinear programming problems. Finally, we show that the nonlinear programming problems can be transformed into integer linear programming problems by introducing additional linear constraints. Examples are used to illustrate the proposed attack strategy.

HaIVFusion: Haze-free Infrared and Visible Image Fusion

Xiang Gao, Yongbiao Gao, Aimei Dong, Jinyong Cheng, Guohua Lv

, Available online , doi: 10.1109/JAS.2024.124926

[Abstract](128) [FullText HTML] (0) [PDF 7007KB](19)

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The purpose of infrared and visible image fusion is to create a single image containing the texture details and significant object information of the source images, particularly in challenging environments. However, existing image fusion algorithms are generally suitable for normal scenes. In the hazy scene, a lot of texture information in the visible image is hidden, the results of existing methods are filled with infrared information, resulting in the lack of texture details and poor visual effect. To address the aforementioned difficulties, we propose a haze-free infrared and visible fusion method, termed HaIVFusion, which can eliminate the influence of haze and obtain richer texture information in the fused image. Specifically, we first design ascene information restoration network (SIRNet) to mine the masked texture information in visible images. Then, adenoising fusion network (DFNet) is designed to integrate the features extracted from infrared and visible images and remove the influence of residual noise as much as possible. In addition, we use color consistency loss to reduce the color distortion resulting from haze. Furthermore, we publish a dataset of hazy scenes for infrared and visible image fusion to promote research in extreme scenes. Extensive experiments show that HaIVFusion produces fused images with increased texture details and higher contrast in hazy scenes, and achieves better quantitative results, when compared to state-of-the-art image fusion methods, even combined with state-of-the-art dehazing methods.

DoS Attack Schedules for Remote State Estimation in CPSs With Two-hop Relay Networks Under Round-Robin Protocol

Shuo Zhang, Lei Miao, Xudong Zhao

, Available online , doi: 10.1109/JAS.2024.124755

[Abstract](117) [FullText HTML] (0) [PDF 368KB](27)

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Hierarchical Secure Steering Control of In-Wheel Motor Driven Electric Vehicle Under Cyber-Physical Constraints

Zifan Gao, Dawei Zhang, Shuqian Zhu

, Available online

[Abstract](11) [FullText HTML] (0) [PDF 669KB](4)

Abstract:

Nash Bargaining Solution-Based Multi-Objective Model Predictive Control for Constrained Interactive Robots

Minglei Zhu, Jun Qi

, Available online

[Abstract](11) [FullText HTML] (0) [PDF 691KB](3)

Abstract:

MFAINet: Multi-Receptive Field Feature Fusion With Attention-Integrated for Polyp Segmentation

Guangzu Lv, Bin Wang, Cunlu Xu, Weiping Ding, Jun Liu

, Available online , doi: 10.1109/JAS.2025.125408

[Abstract](55) [FullText HTML] (0) [PDF 29848KB](12)

Abstract:
Colorectal cancer has become a global public health concern. Removing polyps before they become malignant can effectively prevent the onset of colorectal cancer. Currently, multi-receptive field feature extraction and attention mechanisms have achieved significant success in polyp segmentation. However, how to effectively fuse these mechanisms and fully leverage their respective strengths remains an open problem. In this paper, we propose a polyp segmentation network, MFAINet. We design an attention-integrated multi-receptive field feature extraction module (AMFE), which uses layering and multiple weightings to fuse the multi-receptive field feature extraction and attention mechanisms, maximizing the extraction of both global and detailed information from the image. To ensure that the input to AMFE contains richer target feature information, we introduce a multi-layer progressive fusion module (MPF). MPF progressively merges features at each layer, fully integrating contextual information. Finally, we employ the selective fusion module (SFM) to combine the high-level features produced by AMFE, resulting in an accurate polyp segmentation map. To evaluate the learning and generalization capabilities of MFAINet, we conduct experiments on five widely-used public polyp datasets using four evaluation metrics. Notably, our model achieves the best results in nearly all cases. The source code is available at: https://github.com/MFAINet.

Prescribed Performance Control of Nonlinear Systems With Unknown Sign-Switching Virtual Control Coefficients

Jin-Zi Yang, Jin-Xi Zhang, Tianyou Chai

, Available online

[Abstract](22) [FullText HTML] (0) [PDF 1593KB](16)

Abstract:
The problem of high-performance tracking control for the lower-triangular systems with unknown sign-switching virtual control coefficients as well as unmatched disturbances is investigated in this paper. Instead of the online estimation algorithm, the sliding mode method and the Nussbaum gain technique, a group of orientation functions are employed to handle the unknown sign-switching virtual control coefficients. The control law is combined with the orientation functions and the barrier functions lumped in a recursive manner. It achieves output tracking with the preassigned rate, overshoot, and accuracy. In contrast with the existing solutions, it is effective for the nearly model-free case, with the requirement for information of neither the system nonlinearities nor their bounding functions of the plant, nor the bounds of the disturbances. In addition, our controller exhibits significant simplicity, without parameter identification, disturbance estimation, function approximation, derivative calculation, dynamic surfaces, or command filtering. Two simulation examples are conducted to substantiate the efficacy and advantages of our approach.

Secure Consensus Control on Multi-Agent Systems Based on Improved PBFT and Raft Blockchain Consensus Algorithms

Jing Zhu, Chengfang Lu, Juanjuan Li, Fei-Yue Wang

, Available online

[Abstract](55) [FullText HTML] (0) [PDF 1572KB](8)

Abstract:
There has been significant recent research on secure control problems that arise from the open and complex real-world industrial environments. This paper focuses on addressing the issue of secure consensus control in multi-agent systems (MASs) under malicious attacks, utilizing the practical Byzantine fault tolerance (PBFT) and Raft consensus algorithm in blockchain. Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs, our approach introduces a node grouping methodology based on system topology. Additionally, we utilize the PBFT consensus algorithm for intergroup leader identity verification, effectively reducing the communication complexity of PBFT in large-scale networks. Furthermore, we enhance the Raft algorithm through cryptographic validation during followers’ log replication, which enhances the security of the system. Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents. Through extensive simulations, we demonstrate robust convergence, particularly in scenarios with the relaxed topological requirements. Comparative experiments also validate the algorithm’s lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced (MSR) algorithm.

Event-Triggered Adaptive Control of Noncanonical Nonlinear Systems With Hysteresis Inputs

Guanyu Lai, Kairong Zeng, Yonghua Wang, Tao Zhang, Hanzhen Xiao

, Available online , doi: 10.1109/JAS.2025.125339

[Abstract](66) [FullText HTML] (0) [PDF 1343KB](22)

Abstract:

Personalized Differential Privacy Graph Neural Network

Yanli Yuan, Dian Lei, Chuan Zhang, Zehui Xiong, Chunhai Li, Liehuang Zhu

, Available online , doi: 10.1109/JAS.2025.125279

[Abstract](63) [FullText HTML] (0) [PDF 518KB](7)

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Adaptive Sliding Mode Control With Linear Extended State Observer for Active Magnetic Bearing-Rotor Systems

Yaozhong Zheng, Hai-Tao Zhang, Ziheng Yu, Xiang Huang, Haichao Jiao, Han Ding

, Available online , doi: 10.1109/JAS.2024.125037

[Abstract](48) [FullText HTML] (0) [PDF 3892KB](13)

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Data-Driven Adaptive P-type Iterative Learning Control for Linear Discrete Time Singular Systems

Ijaz Hussain, Xiaoe Ruan, Chuyang Liu, Bingqiang Li

, Available online , doi: 10.1109/JAS.2024.125040

[Abstract](42) [FullText HTML] (0) [PDF 1672KB](10)

Abstract:
Aiming at the pulse response sequence of a kind of repetitive linear discrete-time singular systems unavailable, the paper explores a data-driven adaptive iterative learning control (DDAILC) strategy that interacts with the pulse response iterative correction(PRIC). The mechanism is to formulate the correction performance index as a linear summation of the quadratic correction error of the pulse response and the quadratic tracking error. The correction algorithm of the pulse response arrives and the correction error goes down in a monotonic way. It also discusses the conditional relationship between the declining rate of the correction error and the correction ratio. A DDAILC algorithm is designed by means of substituting the exact pulse response of the GOILC with its approximated one updated in the correction algorithm. The convergences regarding tracking error and correction error are obtained monotonically. Finally, numerical simulation verifies the validity and effectiveness.

ADAPT: A Model-Free Adaptive Optimal Control for Continuum Robots

Haiyang Fang, Sishen Yuan, Hongliang Ren, Shuping He, Shing Shin Cheng

, Available online , doi: 10.1109/JAS.2025.125183

[Abstract](126) [FullText HTML] (0) [PDF 5336KB](44)

Abstract:
Realizing optimal control performance for continuum robots (CRs) poses huge challenges on traditional model-based optimal control approaches due to their high degrees of freedom, complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model. This paper proposes a model-free adaptive optimal control algorithm (ADAPT) for CRs. In our strategy, we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space, wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input. Then, the optimized Hamilton-Jacobi-Bellman (HJB) equation is derived by optimal control principles, and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the origin system dynamics. Under some mild assumptions, the global stability and convergence of the closed-loop control approach are guaranteed. Several simulation experiments are conducted on a magnetic CR (MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm.

An Interpretable Temporal Convolutional Framework for Granger Causality Analysis

Aoxiang Dong, Andrew Starr, Yifan Zhao

, Available online , doi: 10.1109/JAS.2025.125396

[Abstract](189) [FullText HTML] (0) [PDF 4979KB](40)

Abstract:
Most existing parametric approaches for detecting linear or nonlinear Granger causality (GC) face challenges in estimating appropriate time delays, a critical factor for accurate GC detection. This issue becomes particularly pronounced in nonlinear complex systems, which are often opaque and consist of numerous components or variables. In this paper, we propose a novel temporal convolutional network (TCN)-based end-to-end GC detection approach called the Interpretable Temporal Convolutional Framework (ITCF). Unlike conventional deep learning models, which act like a “black box” and are difficult to analyse the interactions between variables, the proposed ITCF is able to detect both linear and nonlinear GC and automatically estimate time delay during the multivariant time series prediction. Specifically, GC is obtained by employing the Least Absolute Shrinkage and Selection Operator (Lasso) regression during the prediction of multivariate time series using TCN. Then, time delays can be estimated by interpreting the TCN kernels. We propose a convolutional Hierarchical Group Lasso (cHGL), a hierarchical regularisation approach to effectively utilise temporal information within each TCN channel for enhanced GC detection. Additionally, as far as we are concerned, this paper is the first to integrate the Iterative Soft-Thresholding Algorithm into the backpropagation of TCN to optimise the proposed cHGL, which enabling causal channel selection and inducing sparsity within each TCN channel to remove redundant temporal information, ultimately creating an end-to-end GC detection framework. The testing results of four experiments, involving two simulations and two real data, demonstrate that the proposed ITCF, in comparison with state-of-the-art, offers a more reliable estimation of GC relationships in complex systems featuring intricate dynamics, limited data lengths, or numerous variables.

Environment Modeling for Service Robots From a Task Execution Perspective

Ying Zhang, Guohui Tian, Cui-Hua Zhang, Changchun Hua, Weili Ding, Choon Ki Ahn

, Available online , doi: 10.1109/JAS.2025.125168

[Abstract](82) [FullText HTML] (0) [PDF 15721KB](15)

Abstract:
Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted.

Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence

Sitan Li, Chien Chern CHEAH

, Available online , doi: 10.1109/JAS.2025.125171

[Abstract](91) [FullText HTML] (0) [PDF 1528KB](21)

Abstract:
Convolutional Neural Networks (CNNs) have shown remarkable success across numerous tasks such as image classification, yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements. In this paper, the first filter learning framework with convergence-guaranteed learning laws for End-To-End learning of deep CNNs is proposed. Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks. The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors. Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods. This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training.

A Decision Variables Classification-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems

Xuanxuan Ban, Jing Liang, Kangjia Qiao, Kunjie Yu, Yaonan Wang, Jinzhu Peng, Boyang Qu

, Available online , doi: 10.1109/JAS.2025.125276

[Abstract](200) [FullText HTML] (0) [PDF 18219KB](50)

Abstract:
Solving constrained multi-objective optimization problems (CMOPs) is a challenging task due to the presence of multiple conflicting objectives and intricate constraints. In order to better address CMOPs and achieve a balance between objectives and constraints, existing constrained multi-objective evolutionary algorithms (CMOEAs) predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints, and the designed strategies usually are effective for the problems with simple constraints. However, these methods most ignore the relationship between decision variables and constraints. In fact, the essence of optimization is to find appropriate decision variables to meet various complex constraints. Therefore, it is hoped that the problem can be analyzed from the perspective of decision variables, so as to obtain more excellent results. Based on the above motivation, this paper proposes a decision variables classification approach, according to the relationship between decision variables and constraints, variables are divided into constraint-related (CR) variables and constraint-independent (CI) variables. Consequently, by optimizing these two types of variables independently, the population can sustain a favorable balance between feasibility and diversity. Furthermore, specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity. Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.

Distributed Gain Scheduling Dynamic Event-Triggered Semi-Global Leader-Following Consensus of Input Constrained MASs Under Fixed/Switching Topologies

Meilin Li, Tieshan Li, Hongjing Liang

, Available online , doi: 10.1109/JAS.2025.125417

[Abstract](65) [FullText HTML] (0) [PDF 2275KB](12)

Abstract:
In this paper, the semi-global leader-following consensus issue of multi-agent systems with constrained input under fixed and switching topologies is investigated via a distributed gain scheduling dynamic event-triggered method. First, a novel distributed gain scheduling consensus protocol is proposed under fixed topology, which integrates time-varying gain and distributed parameter schedulers. This approach enhances the transient performance of consensus tracking by enlarging the gain parameter through the scheduler, while the reliance of the scheduler on global state information is eliminated via a distributed design method. Subsequently, a distributed dynamic event-triggered mechanism is introduced to reduce the controller updates, while the expression of the inter-event times mitigates its explicit reliance on the system matrix. Additionally, to eliminate the need for real-time monitoring of neighboring agents’ states and continuous communication, a distributed dynamic self-triggered mechanism is developed. Next, our approaches are extended to solve the semi-global leader-following consensus problem under switching topologies. The average dwell time technique is employed to alleviate the limitations on the switching rate among multiple topologies. Finally, the theoretical analysis is validated through simulation results.

DKAMFormer: Domain Knowledge-Augmented Multiscale Transformer for Remaining Useful Life Prediction of Aeroengine

Song Fu, Yue Wang, Lin Lin, Minghang Zhao, Lizheng Zu, Yifan Lu, Feng Guo, Shiwei Suo, Yikun Liu, Sihao Zhang, Shisheng Zhong

, Available online , doi: 10.1109/JAS.2025.125126

[Abstract](203) [FullText HTML] (0) [PDF 117826KB](36)

Abstract:
Transformers have achieved promising results on aeroengine remaining useful life (RUL) prediction, but they still have several limitations: 1) Aeroengine domain knowledge, which contains rich information that can reflect the aeroengine’s health statue, is largely ignored in modeling process; 2) Traditional transformer ignores the valuable degradation information from other time scales. To address these issues, a novel domain knowledge-augmented multiscale transformer (DKAMFormer) is developed by integrating domain knowledge and multiscale learning to improve the prognostic performance and reliability. First, to obtain rich and professional aeroengine domain knowledge, multiple detail and complete knowledge graphs (KGs) are established based on the working principle of aeroengine, including aeroengine structure, components working characteristics and sensor parameters. Second, the domain knowledge contained in KGs is convert to embedded vector by KG representative learning, which are then utilized to strengthen and enrich the original multidimensional time-series (MTS) monitoring data, aiming to intergrade domain knowledge and monitoring data to train DKAMFormer. Third, to learn rich and complementary degradation features, a novel multiscale time scale-guided self-attention (MTSGSA) mechanism is designed, which maps original MTS into different time-scale feature spaces, and then employs multiple independent self-attention head to extract the degradation features from different time-scale spaces. Finally, through a series of comparative experiments on the public CMAPSS and N-CMAPSS datasets and compared with 17 SOTA methods, the developed DKAMFormer significantly improves the RUL prediction performance under multiple operation conditions and degradation modes.

Adaptive Dual-Loop Disturbance Observer-based Robust Model Predictive Tracking Control for Autonomous Hypersonic Vehicles

Runqi Chai, Tianhao Liu, Shaoming He, Kaiyuan Chen, Yuanqing Xia, Hyo-Sang Shin, Antonios Tsourdos

, Available online , doi: 10.1109/JAS.2025.125291

[Abstract](107) [FullText HTML] (0) [PDF 4076KB](24)

Abstract:
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances, this paper designed a nonlinear robust model predictive control (RMPC) scheme, which can produce near-optimal tracking commands. Unlike the existing designs, the proposed scheme is less conservative and successfully prioritizes the solution optimality. The established RMPC follows a dual-loop structure. Specifically, in the outer feedback loop, the reference attitude angle profiles are optimally tracked, while in the inner feedback loop, the control moment commands are produced by optimally tracking the desired angular rate trajectories. Besides, an adaptive disturbance observer (ADO) is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances. The recursive feasibility of the optimization process, together with the input-to-state stability of the proposed RMPC, is theoretically guaranteed by introducing a tightened control constraint and terminal region. The derived property reveals that our proposal can steer the tracking error within a small region of convergence. Finally, the effectiveness of the proposed scheme is demonstrated by performing simulation studies.

Optimal Sensor Scheduling for Remote State Estimation With Partial Channel Observation

Bowen Sun, Xianghui Cao

, Available online , doi: 10.1109/JAS.2025.125180

[Abstract](67) [FullText HTML] (0) [PDF 417KB](24)

Abstract:

Nonlinear Frictions Identification in Time-Variant Automotive Systems

Davide Tebaldi, Roberto Zanasi

, Available online , doi: 10.1109/JAS.2025.125294

[Abstract](62) [FullText HTML] (0) [PDF 1487KB](5)

Abstract:
In this paper, the problem of nonlinear frictions identification in a class of nonlinear systems embedding different automotive case studies is addressed. The power-oriented modeling of the system dynamics is first addressed. Next, the identification of the nonlinear friction coefficients representing the system losses, which can have different symmetric or asymmetric characteristics, is addressed using a parabolic interpolation. To show the versatility of the procedure, two automotive physical systems composing the vehicle powertrain are considered as case studies for the identification, namely a Full Toroidal Variator and a Gearbox. The novelty of this work consists of the proposal of a general approach to model nonlinear frictions in a wide class of automotive systems, and in their identification using the proposed least-square-based algorithm. With reference to the latter, we also provide a necessary condition to avoid the rank deficiency problem and considerations about how to increase the identification accuracy.

Full Perception Head: Bridging the Gap Between Local and Global Features

Jie Hua, Zhongyuan Wang, Xin Tian, Qin Zou, Jinsheng Xiao, Jiayi Ma

, Available online , doi: 10.1109/JAS.2025.125333

[Abstract](72) [FullText HTML] (0) [PDF 40820KB](10)

Abstract:
Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. In this context, local features capture fine-grained details such as edges and textures, while global features represent the overall structure and long-range relationships within the image. These features are crucial for accurate object detection, yet most existing methods focus on aggregating local and global features, often overlooking the importance of medium-range dependencies. To address this gap, we propose a novel Full Perception Module (FP-Module), a simple yet effective feature extraction module designed to simultaneously capture local details, medium-range dependencies, and long-range dependencies. Building on this, we construct a full perception head (FP-Head) by cascading multiple FP-Modules, enabling the prediction layer to leverage the most informative features. Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization, achieving 2.7−5.7 AP_val gains when integrated into standard object detectors. Notably, the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance. The code will be released soon.

Correlation-Guided Particle Swarm Optimization Approach for Feature Selection in Fault Diagnosis

Ke Chen, Wenjie Wang, Fangfang Zhang, Jing Liang, Kunjie Yu

, Available online , doi: 10.1109/JAS.2025.125306

[Abstract](201) [FullText HTML] (0) [PDF 1821KB](48)

Abstract:
A large number of features are involved in fault diagnosis, and it is challenging to identify important and relative features for fault classification. Feature selection selects suitable features from the fault dataset to determine the root cause of the fault. Particle swarm optimization (PSO) has shown promising results in performing feature selection due to its promising search effectiveness and ease of implementation. However, most PSO-based feature selection approaches for fault diagnosis do not adequately take domain-specific a priori knowledge into account. In this study, we propose a correlation-guided PSO feature selection approach for fault diagnosis that focuses on improving the initialisation effectiveness, individual exploration ability, and population diversity. To be more specific, an initialisation strategy based on feature correlation is designed to enhance the quality of the initial population, while a probability individual updating mechanism is proposed to improve the exploitation ability. In addition, a sample shrinkage strategy is developed to enhance the ability to jump out of local optimal. Results on four public fault diagnosis datasets show that the proposed approach can select smaller feature subsets to achieve higher classification accuracy than other state-of-the-art feature selection methods in most cases. Furthermore, the effectiveness of the proposed approach is also verified by examining real-world fault diagnosis problems.

Unified Output Feedback Based Prescribed Performance Consensus Tracking Control of Heterogeneous Multi-Agent Systems

Dahui Luo, Yujuan Wang, Frank L. Lewis, Yongduan Song

, Available online , doi: 10.1109/JAS.2024.125094

[Abstract](87) [FullText HTML] (0) [PDF 1788KB](34)

Abstract:
This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC).

Finite-Time Sliding-Mode Control for Semi-Markov Systems With Delayed Impulses

Fangmin Ren, Xiaoping Wang, Yangmin Li, Zhigang Zeng

, Available online , doi: 10.1109/JAS.2024.125004

[Abstract](94) [FullText HTML] (0) [PDF 530KB](32)

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Multi-Agent Swarm Optimization With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association

Tai-You Chen, Xiao-Min Hu, Qiuzhen Lin, Wei-Neng Chen

, Available online , doi: 10.1109/JAS.2025.125150

[Abstract](178) [FullText HTML] (0) [PDF 1876KB](38)

Abstract:
With the development of communication and computation capabilities on terminal hardware, it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors. In this work, we study distributed optimization for multi-target localization with measurement-to-measurement association (DM2M), where each sensor only accesses its own measurement data without the association of measurements from other sensors. We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors. Then, we propose a multi-agent swarm optimization method with contribution-based cooperation (MASTER). In MASTER, each sensor maintains a particle swarm to represent candidate solutions of target positions. Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively. To address the bilevel local objective function, we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization. To promote sensors to optimize the global objective, we design a contribution-based cooperation method to guide sensors to learn from their neighbors. Through localization experiments for different target numbers and localization dimensions, the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms.

The Confluence of Evolutionary Computation and Multi-Agent Systems: A Survey

Tai-You Chen, Wei-Neng Chen, Feng-Feng Wei, Xiao-Qi Guo, Wen-Xiang Song, Rui Zhu, Qiuzhen Lin, Jun Zhang

, Available online , doi: 10.1109/JAS.2025.125246

[Abstract](416) [FullText HTML] (0) [PDF 1981KB](113)

Abstract:
Both evolutionary computation (EC) and multi-agent systems (MAS) study the emergence of intelligence through interaction and cooperation of a group of individuals. EC focuses on solving various complex optimization problems, while MAS provides a flexible model for distributed artificial intelligence. Since their group interaction mechanisms can be borrowed from each other, many studies have made attempts to combine EC and MAS. With the rapid development of Internet of Things, the confluence of EC and MAS has become more and more important, and related articles have shown a continuously growing trend during the last decades. In this survey, we first elaborate on the mutual assistance of EC and MAS from two aspects, agent-based EC and EC-assisted MAS. Agent-based EC aims to introduce characteristics of MAS into EC to improve the performance and parallelism of EC, while EC-assisted MAS aims to use EC to better solve optimization problems in MAS. Furthermore, we review studies that combine the cooperation mechanisms of EC and MAS, which greatly leverage the strengths of both sides. A description framework is built to elaborate existing studies. Promising future research directions are also discussed in conjunction with emerging technologies and real-world applications.

Text2UA: Automatic OPC UA Information Modeling from Textual Data with Large Language Model

Rongkai Wang, Chaojie Gu, Shibo He, Jiming Chen

, Available online , doi: 10.1109/JAS.2025.125114

[Abstract](146) [FullText HTML] (0) [PDF 8118KB](18)

Abstract:

Applications of Domain Generalization to Machine Fault Diagnosis: A Survey

Yongyi Chen, Dan Zhang, Ruqiang Yan, Min Xie

, Available online , doi: 10.1109/JAS.2025.125120

[Abstract](148) [FullText HTML] (0) [PDF 2647KB](33)

Abstract:
In actual industrial scenarios, the variation of operating conditions, the existence of data noise, and failure of measurement equipment will inevitably affect the distribution of perceptive data. Deep learning-based fault diagnosis algorithms strongly rely on the assumption that source and target data are independent and identically distributed, and the learned diagnosis knowledge is difficult to generalize to out-of-distribution data. Domain generalization (DG) aims to achieve the generalization of arbitrary target domain data by using only limited source domain data for diagnosis model training. The research of DG for fault diagnosis has made remarkable progress in recent years and lots of achievements have been obtained. In this article, for the first time a comprehensive literature review on DG for fault diagnosis from a learning mechanism-oriented perspective is provided to summarize the development in recent years. Specifically, we first conduct a comprehensive review on existing methods based on the similarity of basic principles and design motivations. Then, the recent trend of DG for fault diagnosis is also analyzed. Finally, the existing problems and future prospect is performed.

Adaptive Fault-Tolerant Consensus Tracking Control for Nonlinear Multi-Agent Systems With Double Semi-Markovian Switching Topologies and Unknown Control Directions

Chao Zhou, Zehui Mao, Bin Jiang, Xing-Gang Yan

, Available online , doi: 10.1109/JAS.2025.125285

[Abstract](79) [FullText HTML] (0) [PDF 1845KB](17)

Abstract:
This paper is concerned with adaptive consensus tracking control of nonlinear multi-agent systems with actuator faults and unknown nonidentical control directions under double semi-Markovian switching topologies. Considering the complex working environment and the stability differences in communication links between leaders and followers, a double semi-Markov process is first introduced to describe the random switching of communication topologies in the leader-follower structure. In order to address challenges from the unknown nonidentical control directions and partial loss of effectiveness actuator faults, a completely independent parameter is introduced into the Nussbaum function to overcome the inherent obstacle of mutual cancellation and avoid the rapid growth rate. Considering only the state information of agents is transmitted among the agents, an adaptive distributed fault-tolerant consensus tracking control is proposed based on the double semi-Markovian switching topologies using the designed Nussbaum function. Furthermore, the stability of the closed-loop nonlinear multi-agent systems is analyzed using contradiction argument and Lyapunov theorem, from which the asymptotic consensus tracking in mean square sense can be obtained. A numerical simulation example is provided to verify the effectiveness of the proposed algorithm.

Distributed Event-Triggered Nash Equilibrium Seeking for Aggregative Game With Second-Order Dynamics

Yi Huang, Jian Sun, Qing Fei

, Available online , doi: 10.1109/JAS.2024.124830

[Abstract](125) [FullText HTML] (0) [PDF 447KB](21)

Abstract:

Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances

Yuan Zhao, Mengchao Li, Zhongchang Liu, Lichuan Liu, Shixi Wen, Lei Ding

, Available online , doi: 10.1109/JAS.2025.125186

[Abstract](80) [FullText HTML] (0) [PDF 3255KB](30)

Abstract:
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems (VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data. To reduce the adverse effects of quantization errors on system performance, a coupling sliding mode surface is established for each following vehicle. The radial basis function (RBF) neural networks are employed to approximate the unknown external disturbances. Then, a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles. And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle’s controller parameters is independent of the entire communication topology. The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero. Compared with the existing controller design methods and quantization mechanisms, the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors, driving comfort and fuel economy. Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.

Path Following Control of Uncertain and Underactuated Autonomous Surface Vessels

Yang Wu, Yueying Wang, Zhiguang Feng, Xiangpeng Xie

, Available online , doi: 10.1109/JAS.2024.124713

[Abstract](179) [FullText HTML] (0) [PDF 585KB](55)

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A Survey on Rough Feature Selection: Recent Advances and Challenges

Keyu Liu, Xibei Yang, Weiping Ding, Hengrong Ju, Tianrui Li, Jie Wang, Tengyu Yin

, Available online , doi: 10.1109/JAS.2025.125231

[Abstract](294) [FullText HTML] (0) [PDF 2521KB](87)

Abstract:
Advances in data acquisition and accumulation on a massive scale are fueling “the curse of dimensionality” which may deteriorate the generalization performance of machine learning models. Such a dilemma gives birth to the technique of feature selection excelling in the presence of high-dimensional data. As a specific method based on rough set theory, Rough Feature Selection (RFS) has been widely concerned and fruitfully applied. In this survey, we provide a comprehensive review of RFS algorithms that have proliferated in recent years. Firstly, we briefly introduce some typical rough set models especially neighborhood rough set and fuzzy rough set, as well as representative rough feature evaluation criteria. We then systematically discuss several emerging topics of RFS including accelerated, ensemble, incremental, label ambiguous, weakly-supervised, and multi-granularity RFS. Additionally, we illuminate the regular performance validation scheme of RFS and conduct a number of experiments to present benchmarking results of state-of-the-art RFS algorithms. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities of class imbalance, multi-modal scenario, causality inference, and high-level representation for RFS. By providing in-depth knowledge of RFS, we anticipate this survey will: 1) serve as a guidebook for newcomers intending to delve into RFS and a stepping-stone for researchers and practitioners to solve domain-specific problems; 2) gain insights into the state-of-the-art published findings, triggering a series of breakthroughs in RFS; 3) underscore some challenges ahead of RFS, directing future efforts toward punctuating advances beyond questions currently pursued.

Efficient Knowledge-guided Self-evolving Intelligent Behavioral Control for Autonomous Vehicles

Qiao Peng, Kailong Liu, Jingda Wu, Amir Khajepour

, Available online , doi: 10.1109/JAS.2024.124746

[Abstract](81) [FullText HTML] (0) [PDF 715KB](11)

Abstract:

KT-RC: Kernel Time-Delayed Reservoir Computing for Time Series Prediction

Heshan Wang, Mengmeng Chen, Kunjie Yu, Jing Liang, Zhaomin Lv, Zhong Zhang

, Available online , doi: 10.1109/JAS.2024.124986

[Abstract](155) [FullText HTML] (0) [PDF 9327KB](30)

Abstract:
Reservoir computing (RC) is an efficient recurrent neural network (RNN) method. However, the performance and prediction results of traditional RCs are susceptible to several factors, such as their network structure, parameter setting, and selection of input features. In this study, we employ a kernel time-delayed RC (KT-RC) method for time series prediction. The KT-RC transforms input vectors linearly to obtain a high-dimensional set of time-delayed linear eigenvectors, which are then transformed by various kernel functions to represent the nonlinear characteristics of the input signal. Finally, the Bayesian optimization algorithm adjusts the few remaining weights and kernel parameters to minimize the manual adjustment process. The advantages of KT-RC can be summarized as follows: 1) KT-RC solves the problems of uncertainty in weight matrices and difficulty in large-scale parameter selection in the input and hidden layers of RCs. 2) The KT module can avoid massive reservoir hyperparameters and effectively reduce the hidden layer size of the traditional RC. 3) The proposed KT-RC shows good performance, strong stability, and robustness in several synthetic and real-world datasets for one-step-ahead and multistep-ahead time series prediction. The simulation results confirm that KT-RC not only outperforms some gate-structured RNNs, kernel vector regression models, and recently proposed prediction models but also requires fewer parameters to be initialized and can reduce the hidden layer size of the traditional RCs. The source code is available at https://github.com/whs7713578/RC.

An Emulation Approach to Semi-Global Robust Output Regulation for a Class of Nonlinear Uncertain Systems

Jieshuai Wu, Maobin Lu, Fang Deng, Jie Chen

, Available online , doi: 10.1109/JAS.2024.125085

[Abstract](146) [FullText HTML] (0) [PDF 1184KB](21)

Abstract:
In this paper, we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems. By the emulation approach, we propose a class of sampled-data output feedback control laws to solve this problem. In particular, we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system. Then, we design the internal model based on the sampled error output of the system. Based on the internal model principle, we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system. By proposing the sampled error output feedback control law and by means of Lyapunov analysis, we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem. Finally, we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach.

A Homotopy Method for Continuous-Time Model-Free LQR Control Based on Policy Iteration

Wenwu Fan, Junlin Xiong

, Available online , doi: 10.1109/JAS.2025.125132

[Abstract](111) [FullText HTML] (0) [PDF 1038KB](25)

Abstract:
In recent years, reinforcement learning control theory has been well developed. However, model-free value iteration needs many iterations to achieve the desired precision, and model-free policy iteration requires an initial stabilizing control policy. It is significant to propose a fast model-free algorithm to solve the continuous-time linear quadratic control problem without an initial stabilizing control policy. In this paper, we construct a homotopy path on which each point corresponds to an linear quadratic regulator problem. Based on policy iteration, model-based and model-free homotopy algorithms are proposed to solve the optimal control problem of continuous-time linear systems along the homotopy path. Our algorithms are speeded up using first-order differential information and do not require an initial stabilizing control policy. Finally, several practical examples are used to illustrate our results.

Camera Planning for Physical Safety of Outdoor Electronic Devices: Perspective and Analysis

Qin Su, Lei Shu, Gerhard Petrus Hancke, Kai Huang, Edmond Nurellari, Qingsong Zhao, Nikumani Choudhury, Anakhi Hazarika

, Available online , doi: 10.1109/JAS.2025.125129

[Abstract](199) [FullText HTML] (0) [PDF 2265KB](32)

Abstract:
Camera technology advancement and deployment continue to play a vital role in modern life and production, amongst other capabilities, generating relevant visual information. The challenge of optimizing the use and deployment of camera networks in various applications (e.g., surveillance, traffic monitoring, and public safety to name but just a few) has attracted considerable attention from both academia and industries. Camera planning is the first step in addressing this challenge. The surveillance objectives and scenes of a camera network dictate the modelling and optimization algorithms for camera planning. However, existing reviews have primarily focused on models or optimization algorithms, with insufficient attention given to surveillance scenes. This review aims to bridge this gap by 1) Classifying surveillance scenes into the urban environment and rural outdoor environment and comparing the surveillance requirements and challenges; 2) Summarizing the details of camera coverage optimization in the relevant literature from the perspective of deployment scenes; and 3) Proposing a new surveillance scene—Solar Insecticidal Lamps with the Internet of Things—as a case study to analyze the surveillance requirement and challenges in agricultural outdoor environment. Finally, we state the technical outlook on the physical safety of outdoor electronic devices in agriculture settings and provide insights to draw more attention and effort into this area.

Comprehensive Dynamic Model of Vertical Pneumatic Bellows Actuator System Considering Bidirectional Asymmetric Hysteresis

Huai Xiao, Xuzhi Lai, Qingxin Meng, Jinhua She, Edwardo F. Fukushima, Min Wu

, Available online , doi: 10.1109/JAS.2025.125162

[Abstract](86) [FullText HTML] (0) [PDF 4366KB](9)

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The complex nonlinear characteristics of pneumatic soft actuators, such as asymmetric hysteresis, rate-dependence, and mechanical load-dependence, pose a challenge in accurately modeling their dynamics. To address this challenge, this paper proposes a comprehensive dynamic model aimed at describing bidirectional asymmetric hysteresis, rate-dependent, and mechanical load-dependent characteristics of a vertical pneumatic bellows actuator (PBA) system. The dynamic model contains a hysteresis submodel and a load-dependent dynamic submodel. The hysteresis submodel consists of several sets of weighted double-side play (DSP) and weighted dead-zone (DZ) operators connected in series, and it is used to model the bidirectional asymmetric hysteresis of the system. The load-dependent dynamic submodel is built based on the gated recurrent unit (GRU) neural network, and it is used to fit the nonlinear relationship between the displacement of the system and the frequency of the input air pressure as well as the mechanical load. The model parameters of the hysteresis submodel and the load-dependent dynamic submodel are determined by intelligent optimization method and neural network training method, reseparately. The fitness value (FV) between the output of the dynamic model and the experimental data is calculated to be 96.1736%, demonstrating that the parameters of the dynamic model are valid. We conduct six set of experiments to compare the model output with the experimental data, and calculate the root-mean-square errors and the maximum error, respectively. The experimental results show that, the root-mean-square error remains consistently below 2.7700%, while the maximum error remains below 8.4000% across all experiments, thereby substantiating the validity and generality of the proposed model.

CRDet: An Artificial Intelligence-Based Framework for Automated Cheese Ripeness Assessment From Digital Images

Alessandra Perniciano, Luca Zedda, Cecilia Di Ruberto, Barbara Pes, Andrea Loddo

, Available online , doi: 10.1109/JAS.2024.125061

[Abstract](170) [FullText HTML] (0) [PDF 5395KB](32)

Abstract:
Assessing cheese quality and ripeness is a crucial challenge in the dairy industry, with significant implications for product quality, consumer satisfaction, and economic impact. Traditional evaluation methods relying on visual inspection and human expertise are susceptible to errors and time constraints. This study proposes an innovative approach leveraging machine learning and computer vision techniques for automated cheese ripeness detection to address these limitations.The key contributions of this work include the release of the first comprehensive public dataset of cheese wheel images depicting various products at different ripening stages comprising more than 775 images, CR-IDB, an extensive comparative analysis of the performance of machine learning classifiers trained with features extracted from convolutional neural networks and handcrafted descriptors, along with the evaluation of different feature selection techniques, and finally, a proposal of a novel AI-based framework built upon a Random Forest classifier for cheese ripeness detection, called CRDet.The novelty of CRDet lies in its enforceability across multiple types and dairy industries, which has not been previously addressed in the literature. Unlike earlier methodologies that focused on specific cheese types or relied on subjective visual inspections, this study introduces a comprehensive, noninvasive, and automated approach that demonstrates superior classification performance in differentiating ripeness phases. Thus, it overcomes the limitations of traditional methods and enhances the reliability of cheese ripening assessments.With performance in terms of F1 above 90%, the proposed approach reduces reliance on human expertise, ensuring efficient and reliable evaluation methods for the diverse cheese production landscape. The findings provide valuable insights into the potential of feature selection methods for advancing cheese quality analysis, with implications for the broader dairy industry.

Event-Triggered Adaptive Horizon DMPC for Discrete-Time Coupled Nonlinear Systems

Rui Guo, Jianwen Feng, Jingyi Wang, Yi Zhao

, Available online , doi: 10.1109/JAS.2024.124704

[Abstract](85) [FullText HTML] (0) [PDF 643KB](18)

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FDTs: A Feature Disentangled Transformer for Interpretable Squamous Cell Carcinoma Grading

Pan Huang, Xin Luo

, Available online

[Abstract](194) [FullText HTML] (0) [PDF 6689KB](24)

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Global Sampled-Data Output Feedback Stabilization for Nonlinear Systems via Intermittent Hold

Le Chang, Cheng Fu, Huanjun Zhang

, Available online

[Abstract](132) [FullText HTML] (0) [PDF 1318KB](38)

Abstract:
This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems. The intermittent hold allows the control signal to be held in a portion of each sampled period, which does not require the control to be persistently implemented, and thus has less control time. But, less control time degrades the performance of a continuous-time control system or even destabilizes it, especially when the holding portion is sufficiently small. To tackle this obstacle, we first introduce the notion of activating rate to describe the intermittent hold, and give the sampled-data and intermittent-hold controller based on some tuning parameters. Then it is proved that for any activating rate, these parameters can be designed to achieve the stability of the considered systems under appropriately choosing the sampling size. Finally, simulation examples are given to illustrate the effectiveness of the proposed method.

Convex Optimization-Based Model Predictive Control for Mars Ascent Vehicle Guidance System

Kun Li, Yanning Guo, Guangtao Ran, Yueyong Lyu, Guangfu Ma

, Available online , doi: 10.1109/JAS.2024.124587

[Abstract](116) [FullText HTML] (0) [PDF 486KB](25)

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Improving Control Performance by Cascading Observers: Case of ADRC With Cascade ESO

Ahmed T.-E. Benyahia, Momir Stanković, Rafal Madonski, Oluleke Babayomi, Stojadin M. Manojlović

, Available online , doi: 10.1109/JAS.2024.124995

[Abstract](166) [FullText HTML] (0) [PDF 3229KB](36)

Abstract:
In this paper, we show the performance benefits of connecting multiple observers within a control system. We focus here on a particular observer-based control approach, namely the active disturbance rejection control (ADRC) with cascade extended state observer (ESO). For this framework, we analyze the control performance in terms of quality of observer estimation, reference tracking, disturbance rejection, sensitivity to measurement noise/unmodeled dynamics, and overall stability. A comprehensive frequency response analysis is performed to study the influence of cascading the observers on the selected quality criteria. To make the inquiry beneficial also to practitioners, FPGA-in-the-loop tests are conducted using a guided missiles gimbaled seeker. They validate the theoretical findings in discrete-time settings, where the sampling time and hardware resource requirements become a factor. The results of the investigation are distilled into guidelines for prospective users on when and how a cascade observer structure can be useful for controls.

From Singleton to Collaboration: Robust 3D Cooperative Positioning for Intelligent Connected Vehicles Based on Hybrid Range-Azimuth-Elevation Under Zero-Trust Driving Environments

Zhenyuan Zhang, Heng Qin, Darong Huang, Xin Fang, Mu Zhou, Shenghui Guo

, Available online

[Abstract](348) [FullText HTML] (0) [PDF 9820KB](70)

Abstract:
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles (ICVs), in which vehicle-vehicle relative measurements are integrated to provide stable location-aware services. However, in zero-trust autonomous driving environments, the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance. With this in mind, this paper presents an ultra-wide bandwidth (UWB) based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments. Firstly, to overcome measurement degradation under non-line-of-sight (NLOS) propagation conditions, this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter (DKF) by integrating relative range-azimuth-elevation measurements, unlike the state-of-the-art methods that rely on only one single relative range information to update motion states. More specifically, in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack (either false data injection (FDI) or denial of service (DoS)), we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks. To this end, a singular-value decomposition (SVD)-assisted decoupled DKF algorithm is proposed in this work, in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals. To verify the effectiveness in practical 3D NLOS scenarios, we design an intelligent connected multi-robot platform based on a robot operating system (ROS) and UWB technology. Consequently, extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks, especially in the case of hybrid FDI and DoS attacks. In addition, several critical discussions, including the impact of attack parameters, resilience assessment, and a comparison with event-triggered methods, are provided in this work. Moreover, a demo video has been uploaded in the supplementary materials for a detailed presentation.

GT-A²T: Graph Tensor Alliance Attention Network

Ling Wang, Kechen Liu, Ye Yuan

, Available online , doi: 10.1109/JAS.2024.124863

[Abstract](175) [FullText HTML] (0) [PDF 592KB](36)

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Fuzzy Prescribed-Time Control for Uncertain Nonlinear Pure Feedback Systems

Qidong Li, Changchun Hua, Kuo Li

, Available online , doi: 10.1109/JAS.2024.124848

[Abstract](137) [FullText HTML] (0) [PDF 1116KB](27)

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Federated Experiments: Generative Causal Inference Powered by LLM-based Agents Simulation and RAG-based Domain Docking

De-Yu Zhou, Xiao Xue, Qun Ma, Chao Guo, Li-Zhen Cui, Yong-Lin Tian, Jing Yang, Fei-Yue Wang

, Available online , doi: 10.1109/JAS.2024.124671

[Abstract](249) [FullText HTML] (0) [PDF 682KB](68)

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Data-Driven Iterative Learning Consensus Tracking Based on Robust Neural Models for Unknown Heterogeneous Nonlinear Multiagent Systems With Input Constraints

Chong Zhang, Yunfeng Hu, TingTing Wang, Xun Gong, Hong Chen

, Available online

[Abstract](126) [FullText HTML] (0) [PDF 409KB](23)

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Distributed Optimal Formation Control of Unmanned Aerial Vehicles: Theory and Experiments

Gang Wang, Zhenhong Wei, Peng Li

, Available online , doi: 10.1109/JAS.2024.124518

[Abstract](223) [FullText HTML] (0) [PDF 4081KB](52)

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Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection

Hao Liu, Qing-Long Han, Yuzhe Li

, Available online

[Abstract](196) [FullText HTML] (0) [PDF 1560KB](38)

Abstract:
This paper investigates set-valued state estimation of nonlinear systems with unknown-but-bounded (UBB) noises based on constrained polynomial zonotopes which is utilized to characterize non-convex sets. First, properties of constrained polynomial zonotopes are provided and the order reduction method is given to reduce the computational complexity. Then, the corresponding improved prediction-update algorithm is proposed so that it can be adapted to non-convex sets. Based on generalized intersection, the utilization of set-based estimation for attack detection is analyzed. Finally, an example is given to show the efficiency of our results.

Intra-independent Distributed Resource Allocation Game

Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen

, Available online , doi: 10.1109/JAS.2023.123906

[Abstract](301) [FullText HTML] (0) [PDF 552KB](51)

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Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning

Jia Wu, Yican Lou

, Available online , doi: 10.1109/JAS.2023.123270

[Abstract](269) [FullText HTML] (0) [PDF 703KB](38)

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Supplementary File of “Push-Sum Based Algorithm for Constrained Convex Optimization Problem and Its Potential Application in Smart Grid”

Qian Xu, Zao Fu, Bo Zou, Hongzhe Liu, Lei Wang

, Available online

[Abstract](332) [FullText HTML] (0) [PDF 131KB](1)

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Supplementary Material for “Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes”

Zichao Xing, Xinyu Chen, Xingkai Wang, Weimin Wu, Ruifen Hu

, Available online

[Abstract](385) [FullText HTML] (0) [PDF 8129KB](6)

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