A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

Early Access

Display Method:
Attack-Resilient Distributed Nash Equilibrium Seeking for Networked Games Under Unbounded FDI Attacks: Theory and Experiment
Zhi Feng, Zhexin Shi, Xiwang Dong, Guoqiang Hu, Jinhu Lv
, Available online  , doi: 10.1109/JAS.2025.125486
[Abstract](124) [FullText HTML] (0) [PDF 3557KB](43)
Abstract:
An attack-resilient distributed Nash equilibrium (NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks, i.e., false data injection (FDI) attacks. Different from many existing distributed NE seeking works, it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks. An attack-resilient NE seeking algorithm that is distributed (i.e., independent of global information on the graph’s algebraic connectivity, Lipschitz and monotone constants of pseudo-gradients, or number of players), is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically. Another key characteristic is that FDI attacks are allowed to be unknown and unbounded. By exploiting nonsmooth analysis and stability theory, the global asymptotic convergence of the developed algorithm to the NE is ensured. Moreover, we extend this design to further consider the attack-resilient NE seeking of double-integrator players. Lastly, numerical simulation and practical experiment results are presented to validate the developed algorithms’ effectiveness.
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](269) [FullText HTML] (0) [PDF 29206KB](54)
Abstract:
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.
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](356) [FullText HTML] (0) [PDF 1494KB](77)
Abstract:
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.
A Novel Finite-Time Stability Criterion for Nonlinear Systems Involving Flexible Delayed Impulses
Shuchen Wu, Xiaodi Li, Shiji Song
, Available online  
[Abstract](155) [FullText HTML] (0) [PDF 1118KB](30)
Abstract:
Two-dimensional Model-free Off-policy Optimal Iterative Learning Control for Time-varying Batch Systems
Jianan Liu, Zike Zhou, Jinglin Huang, Wenjing Hong, Jia Shi
, Available online  , doi: 10.1109/JAS.2025.125399
[Abstract](191) [FullText HTML] (0) [PDF 1303KB](24)
Abstract:
Although iterative learning control has been widely used in batch processes, designing an optimal iterative learning control scheme for batch systems with unknown dynamics and time-varying parameters remains an open problem. In this paper, we propose a novel two-dimensional model-free off-policy optimal iterative learning control to achieve optimal control performance for linear time-varying batch systems. First, the one-dimensional state space is expanded to the two-dimensional state space by integrating time and batch information. Then, based on dynamic programming and a recursive algorithm, the framework of two-dimensional model-based optimal iterative learning control is established. Based on this framework, two-dimensional model-free optimal iterative learning control is further developed using model-free Q-learning reinforcement learning. The optimal iterative learning control policy is obtained through online off-policy iteration using historical and online operation data. Meanwhile, a rigorous convergence proof of the model-free optimal iterative learning control law is presented. Finally, the simulation results in the injection molding batch process demonstrate the proposed control scheme’s effectiveness, feasibility, and significant improvement in control performance.
On Analytical Modeling for Fast Multi-Objective Torque Allocation in Over-Actuated IWM Vehicles
Fadel Tarhini, Reine Talj, Moustapha Doumiati
, Available online  , doi: 10.1109/JAS.2025.125261
[Abstract](407) [FullText HTML] (0) [PDF 15129KB](42)
Abstract:
Efficient torque allocation in over-actuated vehicles poses a central challenge in the domain of advanced vehicle control. These vehicles, featuring redundant actuators, provide an exceptional avenue for enhancing performance, stability, and efficiency. This paper presents a pioneering tendency for torque allocation in the context of over-actuated vehicles, particularly in-wheel motor (IWM) driven electric vehicles. We introduce a systematic methodology grounded in analytical modeling, allowing for the efficient reconciliation of multiple, often conflicting objectives. The explicit functions are analytically modeled to enhance stability and energy economy. Additionally, a fuzzy logic-based torque allocation strategy is developed and compared, along with other literature methods, with the analytical models. Simulations are conducted in a joint simulation between Simulink/Matlab and SCANeR Studio vehicle dynamics simulator, followed by validation on a real-world dataset. Our findings elucidate the proficiency of the analytical models on vehicle performance, stability, computational efficiency, and energy consumption.
Universal Intermittent State-Constrained Control Without Feasibility Condition for Nonlinear Systems
Xiaohui Yue, Huaguang Zhang, Jiayue Sun, Xiyue Guo
, Available online  , doi: 10.1109/JAS.2025.125357
[Abstract](154) [FullText HTML] (0) [PDF 4243KB](23)
Abstract:
State constraints in nonlinear systems are commonly pursued by resorting to barrier functions, which enforce constraints over the entire duration of system operation. We propose a universal intermittent state-constrained solution, which not only offers flexibility by activating constraints just during specific time periods of interest to the user, but also successfully accommodates different types of constraint boundaries. The innovative shifting functions are proposed to facilitate seamless transitions between constrained and unconstrained operational phases, resulting in more user-friendly design and implementation. By blending an improved shifting transformation into intermittent constraint design, we construct a universal barrier function upon the constrained states, with which our control strategy removes the limitations on constraint functions and completely obviates the feasibility conditions. Furthermore, a modified fuzzy approximator driven by the prediction error rather than the tracking error achieves decoupling of the control and estimation loops, which not only ensures the estimation performance, but also facilitates proof of stability. Finally, the effectiveness of the proposed scheme is assessed by numerical simulation.
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](162) [FullText HTML] (0) [PDF 2262KB](44)
Abstract:
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.
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](302) [FullText HTML] (0) [PDF 6692KB](63)
Abstract:
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.
Accelerated Distributed Cooperative Energy Management for Integrated Energy Systems
Lining Liu, Yulong Huang, Chao Deng
, Available online  , doi: 10.1109/JAS.2025.125489
[Abstract](283) [FullText HTML] (0) [PDF 3725KB](68)
Abstract:
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.
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](152) [FullText HTML] (0) [PDF 2688KB](15)
Abstract:
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.
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](205) [FullText HTML] (0) [PDF 5749KB](40)
Abstract:
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.
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](255) [FullText HTML] (0) [PDF 29848KB](30)
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.
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](234) [FullText HTML] (0) [PDF 518KB](25)
Abstract:
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](443) [FullText HTML] (0) [PDF 5336KB](77)
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](416) [FullText HTML] (0) [PDF 4979KB](49)
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.
Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence
Sitan Li, Chien Chern CHEAH
, Available online  , doi: 10.1109/JAS.2025.125171
[Abstract](250) [FullText HTML] (0) [PDF 1528KB](30)
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.
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](201) [FullText HTML] (0) [PDF 2275KB](23)
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.
Nonlinear Frictions Identification in Time-Variant Automotive Systems
Davide Tebaldi, Roberto Zanasi
, Available online  , doi: 10.1109/JAS.2025.125294
[Abstract](212) [FullText HTML] (4) [PDF 1487KB](9)
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.
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](440) [FullText HTML] (0) [PDF 1821KB](68)
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.
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](197) [FullText HTML] (0) [PDF 530KB](40)
Abstract:
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](508) [FullText HTML] (0) [PDF 1876KB](74)
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](1016) [FullText HTML] (0) [PDF 1981KB](230)
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.
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](233) [FullText HTML] (0) [PDF 1845KB](33)
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.
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](221) [FullText HTML] (0) [PDF 3255KB](51)
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.
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](774) [FullText HTML] (0) [PDF 2521KB](173)
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.
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](306) [FullText HTML] (0) [PDF 9327KB](40)
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.
FDTs: A Feature Disentangled Transformer for Interpretable Squamous Cell Carcinoma Grading
Pan Huang, Xin Luo
, Available online  
[Abstract](321) [FullText HTML] (0) [PDF 6689KB](32)
Abstract:
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
[Abstract](538) [FullText HTML] (0) [PDF 703KB](49)
Abstract:
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](402) [FullText HTML] (0) [PDF 131KB](1)
Abstract:
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](508) [FullText HTML] (0) [PDF 8129KB](6)
Abstract: