Early Access

Display Method:
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
A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process
Xiaofeng Yuan, Weiwei Xu, Yalin Wang, Chunhua Yang, Weihua Gui
, Available online  , doi: 10.1109/JAS.2024.124578
Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the prediction performance in complex industrial processes. To fully utilize data information in PLS residual subspaces, a deep residual PLS (DRPLS) framework is proposed for quality prediction in this paper. Inspired by deep learning, DRPLS is designed by stacking a number of PLSs successively, in which the input residuals of the previous PLS are used as the layer connection. To enhance representation, nonlinear function is applied to the input residuals before using them for stacking high-level PLS. For each PLS, the output parts are just the output residuals from its previous PLS. Finally, the output prediction is obtained by adding the results of each PLS. The effectiveness of the proposed DRPLS is validated on an industrial hydrocracking process.
Multi-Interval-Aggregation Failure Point Approximation for Remaining Useful Life Prediction
Linchuan Fan, Xiaolong Chen, Shuo Li, Yi Chai
, Available online  , doi: 10.1109/JAS.2024.124593
Fuzzy-Model-Based Finite Frequency Fault Detection Filtering Design for Two-Dimensional Nonlinear Systems
Meng Wang, Huaicheng Yan, Jianbin Qiu, Wenqiang Ji
, Available online  , doi: 10.1109/JAS.2024.124452
This article studies the fault detection filtering design problem for Roesser type two-dimensional (2-D) nonlinear systems described by uncertain 2-D Takagi-Sugeno (T-S) fuzzy models. Firstly, fuzzy Lyapunov functions are constructed and the 2-D Fourier transform is exploited, based on which a finite frequency fault detection filtering design method is proposed such that a residual signal is generated with robustness to external disturbances and sensitivity to faults. It has been shown that the utilization of available frequency spectrum information of faults and disturbances makes the proposed filtering design method more general and less conservative compared with a conventional non-frequency based filtering design approach. Then, with the proposed evaluation function and its threshold, a novel mixed finite frequency $ {\cal{H}}_{\infty}/{\cal{H}}_{-}$ fault detection algorithm is developed, based on which the fault can be immediately detected once the evaluation function exceeds the threshold. Finally, it is verified with simulation studies that the proposed method is effective and less conservative than conventional non-frequency and/or common Lyapunov function based filtering design methods.
Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach
Xinli Shi, Xiangping Xu, Guanghui Wen, Jinde Cao
, Available online  , doi: 10.1109/JAS.2023.124089
The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a unified approach for designing FxT gradient flows (FxTGFs). First, a general class of nonlinear functions in designing FxTGFs is provided. A unified method for designing first-order FxTGFs is shown under Polyak-Łjasiewicz inequality assumption, a weaker condition than strong convexity. When there exist both bounded and vanishing disturbances in the gradient flow, a specific class of nonsmooth robust FxTGFs with disturbance rejection is presented. Under the strict convexity assumption, Newton-based FxTGFs is given and further extended to solve time-varying optimization. Besides, the proposed FxTGFs are further used for solving equation-constrained optimization. Moreover, an FxT proximal gradient flow with a wide range of parameters is provided for solving nonsmooth composite optimization. To show the effectiveness of various FxTGFs, the static regret analyses for several typical FxTGFs are also provided in detail. Finally, the proposed FxTGFs are applied to solve two network problems, i.e., the network consensus problem and solving a system linear equations, respectively, from the respective of optimization. Particularly, by choosing component-wisely sign-preserving functions, these problems can be solved in a distributed way, which extends the existing results. The accelerated convergence and robustness of the proposed FxTGFs are validated in several numerical examples stemming from practical applications.
Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm
Wei Qian, Yanmin Wu, Bo Shen
, Available online  , doi: 10.1109/JAS.2024.124419
This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2 (IT2) fuzzy technique under a differential evolution algorithm. To provide a more reasonable utilization of the constrained communication channel, a novel adaptive memory event-triggered (AMET) mechanism is developed, where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data. Sufficient conditions with less conservative design of the fuzzy imperfect premise matching (IPM) controller are presented by introducing the Wirtinger-based integral inequality, the information of membership functions (MFs) and slack matrices. Subsequently, under the IPM policy, a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 Takagi-Sugeno (T-S) fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect. Finally, simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
A Probabilistic Approach for Predicting Vessel Motion
Qi Hu, Jingyi Liu, Zongyu Zuo
, Available online  , doi: 10.1109/JAS.2024.124536
Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems
Sheng Qi, Rui Wang, Tao Zhang, Weixiong Huang, Fan Yu, Ling Wang
, Available online  , doi: 10.1109/JAS.2024.124548
Sparse large-scale multi-objective optimization problems (SLMOPs) are common in science and engineering. However, the large-scale problem represents the high dimensionality of the decision space, requiring algorithms to traverse vast expanse with limited computational resources. Furthermore, in the context of sparse, most variables in Pareto optimal solutions are zero, making it difficult for algorithms to identify non-zero variables efficiently. This paper is dedicated to addressing the challenges posed by SLMOPs. To start, we introduce innovative objective functions customized to mine maximum and minimum candidate sets. This substantial enhancement dramatically improves the efficacy of frequent pattern mining. In this way, selecting candidate sets is no longer based on the quantity of non-zero variables they contain but on a higher proportion of non-zero variables within specific dimensions. Additionally, we unveil a novel approach to association rule mining, which delves into the intricate relationships between non-zero variables. This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value. We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs. The results demonstrate that our approach achieves competitive solutions across various challenges.
Statistical Process Monitoring Based on Ensemble Structure Analysis
Likang Shi, Chudong Tong, Ting Lan, Xuhua Shi
, Available online  , doi: 10.1109/JAS.2017.7510877
Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols
Weiwei Sun, Xinci Gao, Lusong Ding, Xiangyu Chen
, Available online  , doi: 10.1109/JAS.2023.124161
This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation. For the distributed estimation structure under consideration, an estimation center is not necessary, and the estimator derives its information from itself and neighboring nodes, which fuses the state vector and the measurement vector. In an effort to cut down data conflicts in communication networks, the stochastic communication protocol (SCP) is employed so that the output signals from sensors can be selected. Additionally, a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data. On this basis, sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error. Finally, a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.
Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems
Kangjia Qiao, Jing Liang, Kunjie Yu, Xuanxuan Ban, Caitong Yue, Boyang Qu, Ponnuthurai Nagaratnam Suganthan
, Available online  , doi: 10.1109/JAS.2024.124545
Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective help to find feasible Pareto front. However, most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints, and do not consider judging the relations among constraints and do not utilize the information from promising single constraints. Therefore, this paper attempts to identify promising single constraints and utilize them to help solve CMOPs. To be specific, a CMOP is transformed into a multitasking optimization problem, where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively. Besides, an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships. Moreover, an improved tentative method is designed to find and transfer useful knowledge among tasks. Experimental results on three benchmark test suites and 11 real-world problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.
Optimal Secure Control of Networked Control Systems Under False Data Injection Attacks: A Multi-Stage Attack-Defense Game Approach
Dajun Du, Yi Zhang, Baoyue Xu, Minrui Fei
, Available online  , doi: 10.1109/JAS.2023.124005
Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems
Babak Esmaeili, Hamidreza Modares
, Available online  , doi: 10.1109/JAS.2024.124479
This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled using linear parameter-varying (LPV) systems. A model-based probabilistic safe controller is first designed to guarantee probabilistic $\lambda$-contractivity (i.e., stability and invariance) of the LPV system with respect to a given polyhedral safe set. To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model, its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable. It is shown that the variance of the closed-loop system, as well as the probability of safety satisfaction, depends on the decision variable and the noise covariance. A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level. This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set, and thus minimizes the risk of safety violation. Unlike the certainty-equivalent approach that results in a risk-neutral control design, the minimum-variance method leads to a risk-averse control design. It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation. Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.
Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey
Minfeng Qi, Ziyuan Wang, Qing-Long Han, Jun Zhang, Shiping Chen, Yang Xiang
, Available online  , doi: 10.1109/JAS.2022.106058
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization
Zhong-Hua Pang, Yumo Zhang, Xueyuan Sun, Shengnan Gao, Guo-Ping Liu
, Available online  
Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control
Yisha Li, Ya Zhang, Xinde Li, Changyi Sun
, Available online  
This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system. A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency. Firstly a regional multi-agent Q-learning framework is proposed, which can equivalently decompose the global Q value of the traffic system into the local values of several regions. Based on the framework and the idea of human-machine cooperation, a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to real-time traffic flow densities. In order to achieve better cooperation inside each region, a lightweight spatio-temporal fusion feature extraction network is designed. The experiments in synthetic, real-world and city-level scenarios show that the proposed RegionSTLight converges more quickly, is more stable, and obtains better asymptotic performance compared to state-of-the-art models.
A Survey on Type-3 Fuzzy Logic Systems and Their Control Applications
Oscar Castillo, Fevrier Valdez, Patricia Melin, Weiping Ding
, Available online  , doi: 10.1109/JAS.2024.124530
In this paper, we offer a review o.lpe-3 fuzzy logic systems and their applications in control. The main objective of this work is to observe and analyze in detail the applications in the control area using type-3 fuzzy logic systems. In this case, we review their most important applications in control and other related topics with type-3 fuzzy systems. Intelligent algorithms have been receiving increasing attention in control and for this reason a review in this area is important. This paper reviews the main applications that make use of Intelligent Computing methods. Specifically, type-3 fuzzy logic systems. The aim of this research is to be able to appreciate, in detail, the applications in control systems and to point out the scientific trends in the use of Intelligent Computing techniques. This is done with the construction and visualization of bibliometric networks, developed with VosViewer Software, which it is a free Java-based program, mainly intended to be used for analyzing and visualizing bibliometric networks. With this tool, we can create maps of publications, authors, or journals based on a co-citation network or construct maps of keywords, countries based on a co-occurrence networks, research groups, etc.
The First Five Years of a Phase Theory for Complex Systems and Networks
Dan Wang, Wei Chen, Li Qiu
, Available online  , doi: 10.1109/JAS.2024.124542
In this paper, we review the development of a phase theory for systems and networks in its first five years, represented by a trilogy: Matrix phases and their properties; The MIMO LTI system phase response, its physical interpretations, the small phase theorem, and the sectored real lemma; The synchronization of a multi-agent network using phase alignment. Towards the end, we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.
Optimal Positioning Strategy for Multi-Camera, Zooming Drones
Manuel Vargas, Carlos Vivas, Teodoro Alamo
, Available online  , doi: 10.1109/JAS.2024.124455
In the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.
A Generalized Array Factor for Time-Modulated Hexagonal Based Antenna Array Geometry With Novel Trapezoidal Switching
Gopi Ram
, Available online  , doi: 10.1109/JAS.2024.124458
The concept of the time-modulated array has been emerging as an alternative to the complex phase shifters, which lowers the cost of the array feeding network due to the utilization of radio frequency (RF) switches. The various forms of hexagonal antenna array geometries can be used for applications like surveillance tracking in phased array radar and wireless communication systems. This work proposes the generalized array factor (AF) for the hexagonal antenna array geometry based on time modulation. The time modulation in generalized hexagonal geometry can maintain the fixed static amplitude excitation, giving more flexibility over time. Furthermore, a novel trapezoidal switching function is also proposed and applied to the generalized array factor to enable future researchers to use this array factor in the field of advancement to observe how switching schemes like trapezoidal and rectangular affect the array pattern’s side lobe level (SLL). The generalized equation can be utilized for the analysis and synthesis of radiation characteristics of the time-modulated hexagonal array (TMHA), time-modulated concentric hexagonal array (TMCHA), time-modulated hexagonal cylindrical array (TMHCA), and time-modulated hexagonal concentric cylindrical array (TMHCCA). The numerical result illustrates the generation of AF of time-modulated hexagonal structures and also shows that the trapezoidal switching sequence outperforms the rectangular switch using the cat swarm optimization (CSO) approach.
Non-Singular Practical Fixed-time Prescribed Performance Adaptive Fuzzy Consensus Control for Multi-Agent Systems Based on an Observer
Chi Ma, Dianbiao Dong
, Available online  , doi: 10.1109/JAS.2024.124428
In this paper, the problem of non-singular fixed-time control with prescribed performance is studied for multi-agent systems characterized by uncertain states, nonlinearities, and non-strict feedback. To mitigate the nonlinearity, a fuzzy logic algorithm is applied to approximate the intrinsic dynamics of the system. Furthermore, a fuzzy logic system state observer based on leader state information is designed to address the partial unobservability of followers. Subsequently, the power integral method is incorporated into the backstepping approach to avoid singularities in the fixed-time controller. A command filter method is introduced into the standard backstepping approach to reduce the computational complexity of controller design. Then, a non-singular fixed-time adaptive control strategy with prescribed performance is proposed by constraining the tracking error within a prescribed range. Rigorous theoretical analysis ensures the convergence of consensus error in the multi-agent system to the prescribed performance region within a fixed time. Finally, the practicality of the algorithm is validated through numerical simulations.
Distributed Finite-Time Formation Control of Multiple Mobile Robot Systems Without Global Information
Xunhong Sun, Haibo Du, Weile Chen, Wenwu Zhu
, Available online  , doi: 10.1109/JAS.2023.123981
Safety-Critical Trajectory Tracking for Mobile Robots With Guaranteed Performance
Wentao Wu, Di Wu, Yibo Zhang, Shukang Chen, Weidong Zhang
, Available online  , doi: 10.1109/JAS.2023.123864
A Distributed Adaptive Second-Order Latent Factor Analysis Model
Jialiang Wang, Weiling Li, Xin Luo
, Available online  , doi: 10.1109/JAS.2024.124371
Accumulative-Error-Based Event-Triggered Control for Discrete-Time Linear Systems: A Discrete-Time Looped Functional Method
Xian-Ming Zhang, Qing-Long Han, Xiaohua Ge, Bao-Lin Zhang
, Available online  , doi: 10.1109/JAS.2024.124476
This paper is concerned with event-triggered control of discrete-time systems with or without input saturation. First, an accumulative-error-based event-triggered scheme is devised for control updates. When the accumulated error between the current state and the latest control update exceeds a certain threshold, an event is triggered. Such a scheme can ensure the event-generator works at a relatively low rate rather than falls into hibernation especially after the system steps into its steady state. Second, the looped functional method for continuous-time systems is extended to discrete-time systems. By introducing an innovative looped functional that links the event-triggered scheme, some sufficient conditions for the co-design of control gain and event-triggered parameters are obtained in terms of linear matrix inequalities with a couple of tuning parameters. Then, the proposed method is applied to discrete-time systems with input saturation. As a result, both suitable control gains and event-triggered parameters are also co-designed to ensure the system trajectories converge to the region of attraction. Finally, an unstable reactor system and an inverted pendulum system are given to show the effectiveness of the proposed method.
Semi-Decentralized Convex Optimization on \begin{document}$ {\cal{SO}}(3)$\end{document}
Weijian Li, Peng Yi
, Available online  , doi: 10.1109/JAS.2024.124356
New Controllability Criteria for Linear Switched and Impulsive Systems
Jiayuan Yan, Bin Hu, Zhi-Hong Guan, Yandong Hou, Lei Shi
, Available online  , doi: 10.1109/JAS.2024.124272
Cognitive Navigation for Intelligent Mobile Robots: A Learning-Based Approach With Topological Memory Configuration
Qiming Liu, Xinru Cui, Zhe Liu, Hesheng Wang
, Available online  , doi: 10.1109/JAS.2024.124332
Autonomous navigation for intelligent mobile robots has gained significant attention, with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory. In this paper, we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations. We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation. This tackles the issues of topological node redundancy and incorrect edge connections, which stem from the distribution gap between the spatial and perceptual domains. Furthermore, we propose a differentiable graph extraction structure, the topology multi-factor transformer (TMFT). This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation. Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures. Comprehensive validation through behavior visualization, interpretability tests, and real-world deployment further underscore the adaptability and efficacy of our method.
Achieving Given Precision Within Prescribed Time yet With Guaranteed Transient Behavior via Output Based Event-Triggered Control
Zeqiang Li, Yujuan Wang, Yongduan Song
, Available online  , doi: 10.1109/JAS.2023.124134
It is interesting yet nontrivial to achieve given control precision within user-assignable time for uncertain nonlinear systems. The underlying problem becomes even more challenging if the transient behavior also needs to be accommodated and only system output is available for feedback. Several key design innovations are proposed to circumvent the aforementioned technical difficulties, including the employment of state estimation filters with event-triggered mechanism, the construction of a novel performance scaling function and an error transformation. In contrast to most existing performance based works where the stability is contingent on initial conditions and the maximum allowable steady-state tracking precision can only be guaranteed at some unknown (theoretically infinite) time, in this work the output of the system is ensured to synchronize with the desired trajectory with arbitrarily pre-assignable convergence rate and arbitrarily pre-specified precision within prescribed time, using output only with lower cost of sensing and communication. In addition, all the closed-loop signals are ensured to be globally uniformly bounded under the proposed control method. The merits of the designed control scheme are confirmed by numerical simulation on a ship model.
Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation
Xiaolong Chen, Biao Xu, Manjiang Hu, Yougang Bian, Yang Li, Xin Xu
, Available online  , doi: 10.1109/JAS.2024.124287
Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently. This paper proposes a reinforcement learning (RL) method for autonomous vehicles to navigate unsignalized intersections safely and efficiently. The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning. A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them. A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections. The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem. The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency. Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions. The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.
Scalable Temporal Dimension Preserved Tensor Completion for Missing Traffic Data Imputation with Orthogonal Initialization
Hong Chen, Mingwei Lin, Jiaqi Liu, Zeshui Xu
, Available online  , doi: 10.1109/JAS.2024.124278
A Transfer Learning Framework for Deep Multi-Agent Reinforcement Learning
Yi Liu, Xiang Wu, Yuming Bo, Jiacun Wang, Lifeng Ma
, Available online  , doi: 10.1109/JAS.2023.124173
Set-Valued State Estimation of Nonlinear Discrete-Time Systems and Its Application to Attack Detection
Hao Liu, Qing-Long Han, Yuzhe Li
, Available online  
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.
A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry
Xuerui Zhang, Zhongyang Han, Jun Zhao
, Available online  , doi: 10.1109/JAS.2023.124116
Ingredient optimization plays a pivotal role in the copper industry, for which it is closely related to the concentrate utilization rate, stability of furnace conditions, and the quality of copper production. To acquire a practical ingredient plan, which should exhibit long duration time with sufficient utilization and feeding stability for real applications, an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions. To address the complex challenges posed by this integer programming model, including multiple coupling feeding stages, intricate constraints, and significant non-linearity, a multi-stage differential-multifactorial evolution algorithm is developed. In the proposed algorithm, a differential evolutionary algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model. First, unlike traditional time-consuming serial approaches, the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm (DE) caused by the feeding stability in a parallel manner. Second, a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner. In addition, a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm. Finally, the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted, which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches. It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.
Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach
Tao Chen, Ruiyuan Zhao, Jian Chen, Zichao Zhang
, Available online  , doi: 10.1109/JAS.2023.124158
A Novel Vibration-Based Self-Adapting Method to Acquire Real-Time Following Distance for Virtually Coupled Trains
Qinglai Zhang, Jianmin Gao, Qing Wu, Qinglie He, Libin Tie, Wanming Zhai, Shengyang Zhu
, Available online  , doi: 10.1109/JAS.2024.124326
Virtual coupling (VC) is an emerging technology for addressing the shortage of rail transportation capacity. As a crucial enabling technology, the VC-specific acquisition of train information, especially train following distance (TFD), is underdeveloped. In this paper, a novel method is proposed to acquire real-time TFD by analyzing the vibration response of the front and following trains, during which only onboard accelerometers and speedometers are required. In contrast to the traditional arts of train positioning, this method targets a relative position between two adjacent trains in VC operation, rather than the global positions of the trains. For this purpose, an adaptive system containing three strategies is designed to cope with possible adverse factors in train operation. A vehicle dynamics simulation of a heavy-haul railway is implemented for the evaluation of feasibility and performance. Furthermore, a validation is conducted using a set of data measured from in-service Chinese high-speed trains. The results indicate the method achieves satisfactory estimation accuracy using both simulated and actual data. It has favorable adaptability to various uncertainties possibly encountered in train operation. Additionally, the method is preliminarily proven to adapt to different locomotive types and even different rail transportation modes. In general, such a method with good performance, low-cost, and easy implementation is promising to apply.
Event-Based Networked Predictive Control of Cyber-Physical Systems With Delays and DoS Attacks
Wencheng Luo, Pingli Lu, Changkun Du, Haikuo Liu
, Available online  
A Multi-Constrained Matrix Factorization Approach for Community Detection Relying on Alternating-Direction-Method of Multipliers
Ying Shi, Zhigang Liu
, Available online  
A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs
Yalin Zhang, Zhongxin Liu, Zengqiang Chen
, Available online  , doi: 10.1109/JAS.2024.124236
Battery energy storage systems (BESSs) are widely used in smart grids. However, power consumed by inner impedances and the capacity degradation of each battery unit become particularly severe, which has resulted in an increase in operating costs. The general economic dispatch (ED) algorithm based on marginal cost (MC) consensus is usually a proportional (P) controller, which encounters the defects of slow convergence speed and low control accuracy. In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance, we attempt to design a proportional integral (PI) controller with a reset mechanism (PI+R) to asymptotically promote MC consensus and total power mismatch towards 0 in this paper. To be frank, the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing, which accelerates convergence, improves control accuracy, and avoids overshoot. The eigenvalues of the system under a PI+R controller is well analyzed, ensuring the regularity of the system and enabling the reset mechanism. To ensure supply and demand balance within the isolated BESSs, a centralized reset mechanism is introduced, so that the controller is distributed in a flow set and centralized in a jump set. To cope with Zeno behavior and input delay, a dwell time that the system resides in a flow set is given. Based on this, the system with input delays can be reduced to a time-delay free system. Considering the capacity limitation of the battery, a modified MC scheme with PI+R controller is designed. The correctness of the designed scheme is verified through relevant simulations.
Approximately Bi-Similar Symbolic Model for Discrete-time Interconnected Switched System
Yang Song, Yongzhuang Liu, Wanqing Zhao
, Available online  , doi: 10.1109/JAS.2023.123927
Distributed Finite-Time Event-Triggered Formation Control Based on a Unified Framework of Affine Image
Yan-Jun Lin, Yun-Shi Yang, Li Chai, Zhi-Yun Lin
, Available online  , doi: 10.1109/JAS.2023.123885
Event-Triggered Fault Detection — An Integrated Design Approach Directly Toward Fault Diagnosis Performance
Aibing Qiu, Yu Hu, Jingsong Wu
, Available online  , doi: 10.1109/JAS.2023.124074
Global Stabilization Via Adaptive Event-Triggered Output Feedback for Nonlinear Systems With Unknown Measurement Sensitivity
Yupin Wang, Hui Li
, Available online  , doi: 10.1109/JAS.2023.123984
Synchronous Membership Function Dependent Event-Triggered H Control of T-S Fuzzy Systems Under Network Communications
Bo-Lin Xu, Chen Peng, Wen-Bo Xie
, Available online  , doi: 10.1109/JAS.2023.123729
A Novel Scalable Fault-Tolerant Control Design for DC Microgrids WIth Nonuniform Faults
Aimin Wang, Minrui Fei, Dajun Du, Yang Song
, Available online  , doi: 10.1109/JAS.2023.123918
Intra-independent Distributed Resource Allocation Game
Jialing Zhou, Guanghui Wen, Yuezu Lv, Tao Yang, Guanrong Chen
, Available online  , doi: 10.1109/JAS.2023.123906
Integrating Inventory Monitoring and Capacity Changes in Dynamic Supply Chains with Bi-Directional Cascading Propagation Effects
En-Zhi Cao, Chen Peng, Qing-Kui Li
, Available online  , doi: 10.1109/JAS.2023.123309
Efficient Centralized Traffic Grid Signal Control Based on Meta-Reinforcement Learning
Jia Wu, Yican Lou
, Available online  , doi: 10.1109/JAS.2023.123270
Distributed Platooning Control of Automated Vehicles Subject to Replay Attacks Based on Proportional Integral Observers
Meiling Xie, Derui Ding, Xiaohua Ge, Qing-Long Han, Hongli Dong, Yan Song
, Available online  , doi: 10.1109/JAS.2022.105941
Secure platooning control plays an important role in enhancing the cooperative driving safety of automated vehicles subject to various security vulnerabilities. This paper focuses on the distributed secure control issue of automated vehicles affected by replay attacks. A proportional-integral-observer (PIO) with predetermined forgetting parameters is first constructed to acquire the dynamical information of vehicles. Then, a time-varying parameter and two positive scalars are employed to describe the temporal behavior of replay attacks. In light of such a scheme and the common properties of Laplace matrices, the closed-loop system with PIO-based controllers is transformed into a switched and time-delayed one. Furthermore, some sufficient conditions are derived to achieve the desired platooning performance by the view of the Lyapunov stability theory. The controller gains are analytically determined by resorting to the solution of certain matrix inequalities only dependent on maximum and minimum eigenvalues of communication topologies. Finally, a simulation example is provided to illustrate the effectiveness of the proposed control strategy.
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  
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