Article
Computer Science, Artificial Intelligence
Kuo Li, Changchun Hua, Xiu You, Choon Ki Ahn
Summary: This article addresses the leader-following consensus problem of feedforward stochastic nonlinear multiagent systems with switching topologies. A novel consensus scheme is proposed with a simple design procedure, and its feasibility is checked via numerical simulation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yafeng Li, Ju H. Park, Libing Wu, Qingkai Kong
Summary: This article investigates the distributed output-feedback adaptive fuzzy leader-following consensus for a type of high-order stochastic nonlinear multiagent systems. A novel dynamic gain filter is constructed to compensate unmeasured states, adaptive laws are constructed using the tuning function method, and the uncertain interaction functions are decomposed and approximated by fuzzy logic systems. By designing a dynamic gain filter-based distributed adaptive fuzzy leader-following protocol using the backstepping method, the problem of computing mutually dependent inputs in existing results is completely avoided.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Kewen Li, Yongming Li
Summary: This article investigates the problem of adaptive neural network (NN) optimal consensus tracking control for nonlinear multiagent systems (MASs) with stochastic disturbances and actuator bias faults. By utilizing adaptive dynamic programming (ADP), an adaptive NN optimal consensus fault-tolerant control algorithm is presented, and its effectiveness is proven.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Wencheng Zou, Chao Zhou, Jian Guo, Zhengrong Xiang
Summary: This note proposes a new adaptive protocol to address the leader-following consensus problem for second-order nonlinear multiagent systems with unknown Lipschitz constants of nonlinear terms and switching communication topology. The protocol design does not rely on global information, including the eigenvalues of the Laplacian matrix, yet it is proven to achieve practical leader-following consensus. Additionally, a numerical example is provided to demonstrate the effectiveness of the proposed protocol.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Automation & Control Systems
Hao Jiang, Wei Su, Ben Niu, Huanqing Wang, Jiaming Zhang
Summary: This paper proposes a method to address the problem of adaptive consensus tracking control for distributed nonlinear multi-agent systems with unmodeled dynamics. The design difficulties caused by unknown nonlinearities and unmodeled dynamics are overcome by applying the inherent property of radial basis function neural networks and the introduced dynamics signals. Based on adaptive backstepping methods, a new consensus tracking control protocol is proposed. Simulation results demonstrate the effectiveness of the proposed control protocol.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Wei Wang, Yongming Li, Shaocheng Tong
Summary: This article investigates the leader-follower consensus problem for strict-feedback nonlinear multiagent systems under a dual-terminal event-triggered mechanism. The primary contribution of this article is the development of a distributed estimator-based event-triggered neuro-adaptive consensus control methodology. A theoretical analysis shows that all the closed-loop signals are bounded under the developed control methodology, and the estimation of the tracking error asymptotically converges to zero, i.e., the leader-follower consensus is guaranteed. Furthermore, simulation studies and comparisons are conducted to verify the effectiveness of the proposed control method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Mechanical
Yanan Zhang, Jiacheng Song
Summary: This paper investigates the problem of prescribed performance tracking for nonlinear leader-following multiagent systems and develops a data-driven cooperative adaptive sliding mode controller. By combining the prescribed performance function with the sliding mode surface, the synchronization measurement error is transformed. The agent's dynamics are described as a linearization model using pseudo-partial derivatives. A data-based cooperative adaptive sliding mode control approach is designed to achieve synchronization among all agents, ensuring that the synchronization measurement error converges to a predefined zone. The proposed controller depends solely on the input/output data of the agents and its effectiveness and advantages are validated through numerical simulations.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Kaixin Lu, Zhi Liu, Guanyu Lai, C. L. Philip Chen, Yun Zhang
Summary: This article proposes a new adaptive neural control approach to address the leader-follower consensus control problem of uncertain multiagent systems, aiming to improve system steady state and transient performance. By incorporating smooth functions into backstepping design and Lyapunov analysis, the proposed controller achieves perfect asymptotic consensus performance and tunable L-2 transient performance of synchronization errors, as demonstrated by simulation results.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Shengli Du, Hong Sheng, Hao-Yuan Sun
Summary: This paper investigates the leader-following consensus problem for general linear multiagent systems under a fully distributed manner. It proposes a fully distributed event-based control method that avoids using global information of the communication network. By designing node-based adaptive parameters and introducing internal dynamic variables, the method improves the scalability and flexibility of the system, ensures convergence, and prevents Zeno behavior.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Jiawei Zhuang, Shiguo Peng, Yonghua Wang
Summary: In this article, the leader-following consensus of discrete-time second-order stochastic nonlinear multiagent systems with Markov switching topology is investigated. By utilizing the impulsive control strategy, the leader-following consensus issue is transformed into the stability problem of the impulsive error systems. Sufficient conditions for exponential mean-square stability under fixed and switching topologies are derived, considering the notions of average impulsive interval and ADT.
IEEE SYSTEMS JOURNAL
(2022)
Article
Automation & Control Systems
Yafeng Li, Steven X. Ding, Changchun Hua, Guopin Liu
Summary: This article studies the distributed adaptive failures compensation output-feedback consensus for a class of nonlinear multiagent systems (MASs) with multiactuator failures allowing unmatched redundancy under directed switching graphs. A novel distributed reference generator and reduced-order dynamic gain filter are designed, and a distributed adaptive protocol is proposed to compensate the actuator failures and relax conditions on the communication graph.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Bilal J. Karaki, Magdi S. Mahmoud
Summary: This article studies the consensus problem for discrete-time nonlinear multiagent systems with time delay. A distributed consensus protocol based on an asynchronous event-triggered mechanism is proposed to achieve state consensus for multiagent systems with nonlinear perturbations. The protocol reduces network communication congestion and energy consumption. The theoretical results are illustrated through a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Zengke Jin, Chaoli Wang, Dong Liang, Zongyu Zuo, Zhenying Liang
Summary: This paper discusses the fixed-time leader-following consensus problem for multiple uncertain non-holonomic systems and improves the assumptions made in previous research. By proposing a fixed-time adaptive distributed observer and a novel observer-based distributed switching control law, this paper overcomes nonholonomic constraints and relaxes the assumptions of uncertain functions.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Shaosheng Xu, Jinde Cao, Qingshan Liu, Leszek Rutkowski
Summary: This paper studies the optimization of finite-time consensus in a multiagent system with a leader disturbed by white noise, using constructive and martingale methods to ensure effectiveness. It solves the optimal control problem with piecewise constant gains in a stochastic environment, proposing reformed dynamic principles and HJB PDEs. Additionally, a heuristic method is proposed for high-dimensional multiagent systems, achieving higher precision compared to usual filtering methods in a numerical example.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Xin Ye, Baoyu Wen, Huiyan Zhang, Fangzhen Xue
Summary: This paper investigates distributed iterative learning control for nonholonomic mobile robots with a time-varying reference, addressing parametric uncertainties, lack of full actuation, and unknown control gains. A distributed control scheme based on local compensatory filters and composite energy functions is proposed, ensuring stability and convergence of consensus errors. An example is provided to demonstrate the effectiveness of the designed control law.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Chao Deng, Changyun Wen, Wei Wang, Xinyao Li, Dong Yue
Summary: This article investigates the consensus problem for high-order nonlinear multi-agent systems (MASs) with an uncertain leader under event-triggered communication. It introduces distributed intermediate parameter estimators based on event-triggered communication mechanism to estimate the unknown parameters of the uncertain leader. To ensure the existence of high-order derivatives, the estimators are modified by using the Hermite interpolation method. Moreover, novel high-order filters are proposed to generate local reference signals and ensure the existence of high-order derivatives of the filter states. A backstepping-based decentralized adaptive controller is developed based on the developed filters, and it is proved that consensus errors are asymptotically convergent with the developed method. Simulation examples are provided to demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Huifeng Zhang, Dong Yue, Chunxia Dou, Gerhard P. Hancke
Summary: This article proposes a potential game-based two-layered hierarchical optimization strategy for dealing with competitive relationships among different stakeholders in hybrid energy systems (HESs). A multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm in the upper-level model, while a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized for the lower-level model. The proposed method reduces computational complexity and properly deals with uncertainty problems for the optimal operation of HESs.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Aimin Gong, Xiangpeng Xie, Dong Yue, Jianwei Xia
Summary: This article focuses on developing a featured multi-instant Luenberger-like observer for discrete-time Takagi-Sugeno fuzzy systems with unmeasurable state variables. The observer aims to reduce conservatism and computational complexity. An enhanced gain-scheduling mechanism is proposed, and a different group of observer gain matrices with less conservatism is designed. Furthermore, redundant terms containing surplus and unknown system information are discriminated and removed to reduce the computational complexity.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Electrical & Electronic
Zhanqiang Zhang, Chunxia Dou, Dong Yue, Yudi Zhang, Bo Zhang, Bing Li
Summary: In distribution networks with high PV penetration, intermittent issues can easily lead to regional voltage violations. To address this problem, this paper proposes a regional coordinated voltage regulation algorithm based on sensitivity, using proportional power compensation from PV inverters and battery energy storage systems (BESS). By sharing the burden of voltage regulation among all PV inverters and BESSs, the regional voltage violation issue can be resolved without relying on network-wide controllable resources. The simulation results demonstrate the effectiveness of this algorithm.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Theory & Methods
Songlin Hu, Xiaohua Ge, Yu Li, Xiaoli Chen, Xiangpeng Xie, Dong Yue
Summary: In this paper, a resilient load frequency control (LFC) design method is proposed for multi-area power systems under a new class of time-constrained denial-of-service (DoS) attacks. A time-varying Lyapunov function (TVLF) approach, which is dependent on the attack parameters, is developed to enable a resilient LFC design without compromising system stability and performance. The minimum allowable sleeping period and the maximum allowable active period of the attacked LFC system can be explicitly disclosed. Two simulation case studies are presented to demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Engineering, Electrical & Electronic
Kai Tao, Qiang Wang, Zhikai Yao, Bing Jiang, Dong Yue
Summary: A reliability assessment method for underground rock based on acoustic emission (AE) mutual information was proposed in this research. The mutual relationship between multiple AE parameters and moisture permeation damage was analyzed. The experiment showed that this method could identify samples with different moisture permeation damage levels, predict moisture permeation damage in the early stage, and reduce sensor error through probabilistic analysis.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Huifeng Zhang, Dong Yue, Chunxia Dou, Gerhard P. P. Hancke
Summary: In this paper, a novel multiple time-scale dispatch model is developed to address the challenge of micro-grid dispatch, considering the system security and economic cost with different requirements. A penalty-based boundary intersection (PBI) based multi-objective optimization approach is proposed with an elite learning technique. Simulation results demonstrate that the proposed optimization strategy can achieve minimum economic cost while satisfying voltage and frequency stability.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Huifeng Zhang, Dong Yue, Chunxia Dou, Xiangpeng Xie, Kang Li, Gerhardus P. Hancke
Summary: This article proposes a TSK fuzzy system-based reinforcement learning approach for the resilient optimal defensive strategy of interconnected microgrids. The proposed method utilizes ADMM and PBI-based multiobjective optimization to consider security switching control strategy, economic cost, and emission issues simultaneously.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuqiang Luo, Zidong Wang, Weiguo Sheng, Dong Yue
Summary: This article proposes a delay-range-dependent approach to handle the state estimation problem for delayed impulsive neural networks. A new type of nonlinear function is adopted as the neuron activation function to improve the model's generality. The round-robin protocol is used to alleviate data collisions and unnecessary network congestion, and a state observer is constructed with the aid of the Lyapunov stability theory to ensure asymptotically stable estimation error dynamics.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Xiang-Peng Xie, Jianqiang Lu, Dong Yue, Da-Wei Ding
Summary: This study proposes a real-time gain-scheduling mechanism for enhancing the robust performance of nonlinear fault estimation. By introducing key tunable parameters, different switching modes are generated and exclusive FE gain matrices are designed. Numerical comparisons demonstrate the superiority of the proposed method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Lei Zou, Zidong Wang, Qing-Long Han, Dong Yue
Summary: This article investigates the tracking control problem for a type of linear networked systems subject to the round-Robin protocol scheduling and impulsive transmission outliers. It proposes a novel tracking controller to protect the tracking performance from outliers by removing the contaminated signals. The effectiveness of the developed outlier-resistant tracking control scheme is demonstrated through a simulation example.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Huifeng Zhang, Dong Yue, Chunxia Dou, Yusheng Xue, Gerhard P. Hancke
Summary: This article proposes a two-level optimal control strategy with event-triggered switching mechanisms to address the challenge of optimal security control in isolated power systems. The upper-level model uses event-triggered switching mechanisms to decrease potential risk and ensure system security, while the lower-level model employs switching topology for distributed optimization and minimizing power generation cost.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Tengfei Zhang, Yudi Zhang, Fumin Ma, Chen Peng, Dong Yue, Witold Pedrycz
Summary: This article introduces the fundamental concepts of information granularity and information granules in the field of granular computing, and proposes the principle of justifiable granularity. A two-phase framework based on Fuzzy C-means clustering is developed for designing information granules. However, existing algorithms have the issue of imprecise description of boundary-overlapping data. To address this, the rough k-means clustering is introduced into the framework along with a proposed local boundary fuzzy metric. The algorithm is further improved by refining the principle. Comparative experiments demonstrate the validity and performance of the algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Bo Zhang, Sergey Gorbachev, Chunxia Dou, Victor Kuzin, Ju H. Park, Zhanqiang Zhang, Dong Yue
Summary: A source-storage-load coordinated master-slave control strategy is proposed to address the voltage/frequency issues caused by sudden load disturbance in an isolated microgrid. The stability of system voltage and frequency relies on the stability of the master resource's output. The strategy includes improving operational security, communication reliability, and mode switching stability of resources.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Civil
Guoan Xu, Juncheng Li, Guangwei Gao, Huimin Lu, Jian Yang, Dong Yue
Summary: This paper introduces a lightweight real-time semantic segmentation network called LETNet, which combines U-shaped CNN with Transformer effectively to compensate for respective deficiencies. The elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module simultaneously have a positive impact on training from scratch. Extensive experiments on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
