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distributed Adaptive consensus Protocol for Connected Vehicle Platoon With Heterogeneous Time-Varying Delays and Switching Topologies

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 17620-17631页

作者： Yu, Guokuan Wong, Pak Kin Huang, Wei Zhao, Jing Wang, Xian-Bo Yang, Zhi-Xin Guizhou Normal Univ Sch Mech & Elect Engn Guiyang 550025 Peoples R China Univ Macau Dept Electromech Engn Macau Peoples R China Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Henan Univ Technol Coll Elect Engn Zhengzhou 450001 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective.

关键词： Topology Delays Switches Linear matrix inequalities Connected vehicles Vehicle dynamics Time-varying systems Vehicle platoon switching topology distributed consensus control time-varying delay delay-range-dependent approach

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distributed Adaptive control of Nonlinear Multi-Agent Systems-Based controller Dynamic Linearization Method

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IEEE ACCESS 2025年 13卷 64796-64809页

作者： Wu, Wenchao Fu, Lixia Li, Han Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650031 Peoples R China

The leader-follower consensus tracking control and containment control problems for nonlinear nonaffine multi input multi output (MIMO) multi-agent systems without a leader or with a leader-follower are investigated. Different from the conventional controller based compact form dynamic linearization (CFDL) method for this problem, a new distributed model free adaptive control method (CFFDL-DMFAC) is proposed by using the controller based full form dynamic linearization (CFFDL) method and a new time-varying pseudo Jacobian matrix. This method does not require a mathematical model of the system during the controller design process, and only uses real time I/O data between neighboring agents to achieve the common goal of consensus tracking of each agent. At the same time, the use of consensus error dynamic linearization methods enables the algorithm design and stability analysis of containment control to be analyzed in a unified manner with leaderless and leader-follower consensus control. Finally, rigorous mathematical proofs and simulation results are used to verify the convergence and effectiveness of the proposed control scheme.

关键词： Mathematical models Vectors consensus control Stability analysis Multi-agent systems Adaptation models control systems Topology Data models Convergence Data-driven control controller dynamic linearization multi-agent systems distributed consensus control

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Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults

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NEUROCOMPUTING 2019年 349卷 64-76页

作者： Li, Yu Wang, Chaoli Cai, Xuan Li, Lin Wang, Gang Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Univ Nevada Dept Elect & Biomed Engn Reno NV 89557 USA

This paper investigates the consensus asymptotic convergence problem for a class of nth-order strict-feedback multiagent systems, which include the input quantization, actuator faults, unknown nonlinear functions and directed communication topology. Because the upper bounds of the time-varying stuck faults and external disturbance are commonly difficult to accurately determine, it is also assumed that these upper bounds are unknown in this paper. First, a group of first-order filters are designed to estimate the bounds of the reference signal for each agent. Second, smooth functions are introduced to compensate the effect of quantization and bounded stuck faults. Meanwhile, a new back-stepping method is used to propose an intermediate control law and an adaptive design procedure, and the final distributed control protocols are established. All closed-loop signals are uniformly bounded, and the tracking errors asymptotically converge to zero. Finally, a practical example simulation is provided to demonstrate the effectiveness of the proposed scheme. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed consensus control Neural networks Input quantization Actuator fault consensus asymptotic convergence

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distributed control in Uncertain Nonlinear Multiagent Systems Under Event-Triggered Communication and General Directed Graphs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 599-609页

作者： Wang, Gang Zuo, Zongyu Li, Peng Univ Shanghai Sci & Technol Inst Machine Intelligence Shanghai 200093 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Harbin Inst Technol Shenzhen Sch Mech Engn & Automat Shenzhen 518055 Peoples R China

Designing a consensus algorithm for multiagent systems within an event-triggered communication setting is challenging due to the discontinuous and inaccurate interaction information caused by event-triggering mechanisms. Currently, most related results require an undirected or balanced directed graph. To avoid such restrictive requirements and consider general directed graphs with a spanning tree, we first investigate the perturbed consensus problem of first-order dynamics. Then, we extend our findings to address the consensus problem of the uncertain nonlinear multiagent systems described in Lagrangian dynamics, Brunovsky dynamics, and strict-feedback dynamics under event-triggered communication. We develop three distributed consensus protocols that consider the unique characteristics of these systems and assign different reference signals accordingly. Our proposed schemes ensure that consensus errors either converge to zero or to a small adjustable neighborhood around zero without Zeno behavior while preserving signal boundedness in the closed-loop system. Finally, we conduct extensive simulations to further illustrate the efficiency of our theoretical results.

关键词： Directed graphs Multi-agent systems Vehicle dynamics consensus control Information processing Topology Uncertainty distributed consensus control event-triggered control uncertain dynamics

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Human-in-the-Loop consensus control for Multiagent Systems With External Disturbances

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11024-11034页

作者： Ma, Li Zhu, Fanglai Zhao, Xudong Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

关键词： Human in the loop Observers consensus control consensus protocol Hazards Multi-agent systems Topology distributed consensus control full-order observer human-in-the-loop multiagent systems (MASs) unknown input reconstruction (UIR)

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Robust distributed adaptive consensus for discrete-time multiagent systems with uncertain topologies

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NEUROCOMPUTING 2021年 449卷 100-107页

作者： Lin, Chengrong Hu, Bo Huang, Wenchao Niu, Tao Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Fuzhou Univ Fuzhou 350108 Peoples R China

This paper considers the stochastic consensus problem of high-order linear discrete-time multiagent systems (MASs) with uncertain topologies and external disturbances. The randomly switching interaction signed topologies are governed by a time-homogenous Markov chain with incomplete knowledge of the transition probabilities. Based on Lyapunov function theory and adaptive control strategies, we employ a homogeneous parameter-dependent Lyapunov function to propose sufficient conditions under a set of linear matrix inequality (LMI) constraints. A free-weighting matrix method is introduced to give the proposed LMIs extra freedom to search feasible solutions. Furthermore, the adaptive controllers applied in this paper are independent of topologies and fully distributed;i.e., the system achieves robust consensus without continuously updating the controller and dealing with global data. Additionally, we extend the theoretical framework to analyze the disturbance rejection performance of MASs under the stochastic process. Two numerical examples are provided to show the effectiveness of the algorithms. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Multiagent systems distributed consensus control Switching topologies Linear matrix inequality

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consensus of Nonlinear Multiagent Systems With Uncertainties Using Reinforcement Learning Based Sliding Mode control

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第1期70卷 424-434页

作者： Li, Jinna Yuan, Lin Chai, Tianyou Lewis, Frank L. Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA

This paper investigates distributed control protocols design for uncertain nonlinear multi-agent systems with the goal of achieving the optimal consensus. The critical challenges encountered when designing the optimal distributed control protocols are mainly caused by the internal coupling of agents, uncertainty and nonlinear dynamics. Communication delay among agents makes overcoming these challenges even more difficult. To this end, a novel sliding mode control design method is developed based on the sliding mode control principle and the reinforcement learning technique. The remarkable highlights of the developed method in this paper include the design of distributed sliding mode controllers and the integrated framework of sliding mode control and reinforcement learning, which bring the outcome of successfully learning the composite distributed control protocols for multi-agent systems. Thus, all agents can successfully eliminate the negative impacts brought by system uncertainties and communication delay among agents, and finally follow the leader with a nearly optimal approach. The reachability of sliding mode surfaces and the optimal consensus are rigorously proven and analyzed. Finally, simulation results illustrate the effectiveness of the developed method.

关键词： Uncertainty Delays Protocols Sliding mode control Multi-agent systems Robustness Reinforcement learning Multi-agent systems distributed consensus control sliding mode control reinforcement learning

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consensus control of Highway On-Ramp Merging With Communication Delays

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 9127-9142页

作者： Zhao, Chenyang Chu, Duanfeng Wang, Rukang Lu, Liping Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430063 Peoples R China

In the environment of intelligent and connected vehicles, the information sharing level among vehicles and infrastructure get comprehensively improved. Aiming at the cooperative merging control at highway ramp, we present a two-stage hybrid cooperative control framework considering communication time delays including centralized decision-making for grouping with sequencing and distributed consensus control. The centralized controller is based on a grouping strategy and a cost function for minimizing total travel time and delays and determining the optimal vehicle passage order. Then we model the merging vehicles formation as a multi-agent system, which also could be viewed as a distributed controlling system. Combining with the multi-agent consensus method, it could achieve the desired inter-vehicle distance and speed. The proof tool is the Lyapunov-Krasovskii stability theorem in terms of convergence and formation stability. Finally, we design three simulation scenarios through varying the number of vehicles. Meanwhile, in order to verify the resistance of the algorithm to communication time delays, three conditions with respect to no time delays, heterogeneous time delays, and homogeneous time delays are considered. Simulation result verifies the advantage of the proposed method compared with the Hamilton control method. The efficiency of the proposed method is impervious in three different types of communication time delays and has approximately 30%, 25%, 15% improvement over that of the Hamiltonian method, respectively. Furthermore, the adaptation and effectiveness of the proposed method is demonstrated under different volumes of traffic flow and communication delays.

关键词： Merging Delays Delay effects Heuristic algorithms Decision making Sequential analysis Safety distributed consensus control intelligent and connected vehicle multi-agent system vehicle cooperation

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consensus control in Heterogeneous Nonlinear Multiagent Systems With Position Feedback and Switching Topologies

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 3546-3557页

作者： Wang, Gang Univ Shanghai Sci & Technol Inst Machine Intelligence Shanghai 200093 Peoples R China

The primary challenge in designing a consensus algorithm under switching topologies is to deal with discontinuous interactive information. At present, most consensus control results are limited to either agents with mismatched uncertainties in a time-invariant interaction environment or integrator dynamics in a dynamically changing interaction environment. This work addresses consensus problems of heterogeneous nonlinear multiagent systems without these limitations. We propose a novel, simple, yet effective higher-order filter to yield differentiable control variables employing neighbor position information and discontinuous topology information. By applying this filter and defining a suitable auxiliary error, we develop distributed consensus controllers for second-order and uncertain higher-order systems. One of the salient attributes of the proposed methodology is to allow each agent to require minimal local feedback, i.e., only the position information of itself and its neighboring agents. Theoretical and simulation verifications of the scheme guaranteeing asymptotic convergence of consensus and the boundedness of each signal in the closed-loop system are carefully examined.

关键词： Topology Multi-agent systems Switches Uncertainty Observers Nonlinear dynamical systems Network topology Multiagent systems distributed consensus control directed graphs uncertain dynamics

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distributed Virtual Inertia Implementation of Multiple Electric Springs Based on Model Predictive control in DC Microgrids

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第12期69卷 13439-13450页

作者： Yang, Hanqing Li, Tieshan Long, Yue Chen, C. L. Philip Xiao, Yang Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA

Smart load with series-connected dc electric spring (ES) and noncritical load (NCL) structure can compensate for load voltage and improve the power quality. In this article, a distributed virtual inertial control framework based on model predictive control is proposed for low inertia in the dc microgrid. First, the current prediction model of the ES bidirectional full-bridge dc/dc converter is presented. Based on the virtual inertia control, model predictive control is used to optimize virtual capacitance during the operation. When the system suffers from disturbance, virtual capacitance increases to slow down the change of the dc bus voltage. When disturbance happens, the system can be stabilized quickly by reducing the virtual capacitance. Then, the consensus algorithm based on distributed control is established after defining the NCL voltage deviation rate, which can achieve the balance of NCL voltage deviations, and effectively avoid the more significant voltage deviation in one of NCLs. Furthermore, the small-signal model of the proposed control method is developed, and the influence of the proposed controller on the small-signal stability is described by employing the eigenvalue analysis method. Finally, based on the RT-Lab hardware-in-the-loop simulation system, comparisons and analysis are made to verify the effectiveness of the proposed control method under power step conditions.

关键词： DC electric spring distributed consensus control model predictive control virtual inertia

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