Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults DOI Creative Commons
Zhenyou Wang, Xiaoquan Cai, Ao Luo

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 7906 - 7906

Published: Dec. 11, 2024

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The error converges to bounds in by an improved function and coordinate transformation. Due unknown faults sensors, system states cannot be gained correctly; therefore, adaptive compensation strategy is constructed based on approximation capabilities of neural networks solve negative impact failures. reinforcement-learning-based backstepping proposed realize system. Utilizing Lyapunov stability theory, it shown that designed controller enables converge fixed time all signals closed-loop are bounded probability. Finally, simulation results prove effectiveness method.

Language: Английский

ADP-Based Fault-Tolerant Control for Multiagent Systems With Semi-Markovian Jump Parameters DOI
Lulu Zhang, Huaguang Zhang, Jiayue Sun

et al.

IEEE Transactions on Cybernetics, Journal Year: 2024, Volume and Issue: 54(10), P. 5952 - 5962

Published: July 11, 2024

This article analyzes and validates an approach of integration adaptive dynamic programming (ADP) fault-tolerant control (FTC) technique to address the consensus problem for semi-Markovian jump multiagent systems having actuator bias faults. A process, a more versatile stochastic is employed characterize parameter variations that arise from intricacies environment. The reliance on accurate knowledge system dynamics overcome through utilization actor-critic neural network structure within ADP algorithm. data-driven FTC scheme introduced, which enables online adjustment automatic compensation It has been demonstrated signals generated by controlled exhibit uniform boundedness. Additionally, followers' states can achieve maintain with leader. Ultimately, simulation results are given demonstrate efficacy designed theoretical findings.

Language: Английский

Citations

5
Data-Driven Fault-Tolerant Consensus Control for Constrained Nonlinear Multiagent Systems via Adaptive Dynamic Programming DOI
Lulu Zhang, Shuo Liu, Tianbiao Wang

et al.

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 121976 - 121976

Published: Feb. 1, 2025

Language: Английский

Citations

0
Bipartite Complete Synchronization of Fractional Heterogeneous Networks via Quantized Control Without Gauge Transformation DOI
Yu Sun, Cheng Hu, Juan Yu

et al.

IEEE Transactions on Systems Man and Cybernetics Systems, Journal Year: 2025, Volume and Issue: 55(5), P. 3720 - 3731

Published: March 14, 2025

Language: Английский

Citations

0
Leader-Following Secure Output Consensus of Heterogeneous Multiagent Systems Based on Two Sampling Mechanisms Under Hybrid Cyber-Attacks DOI
Xinchun Jia, Hongpeng Li, Xiaobo Chi

et al.

IEEE Transactions on Cybernetics, Journal Year: 2024, Volume and Issue: 54(12), P. 7826 - 7838

Published: July 23, 2024

This article investigates the leader-following secure output consensus (LFSOC) problem of heterogeneous multiagent systems (MASs) under hybrid cyber-attacks. A novel cyber-attack model consisting aperiodic additive deception (AAD) attacks and denial service (ADoS) is proposed for characterizing cyber-attacks in a real network, where aperiodicity reflected fact that duration each can be different. First, compensator introduced agent to estimate leader's state. Second, two sampling mechanisms multirate (MRS) mechanism periodic are employed MASs. The MRS used obtain real-time sampled data on different physical variables agent. applied sample agents' compensators broadcast their neighbors immediately through network. By selecting an appropriate period (i.e., communication compensators), robustness MASs against enhanced. Then, selected network environments by taking into consideration parameters. Under these mechanisms, sync controller developed achieve LFSOC Finally, example presented verify effectiveness approach.

Language: Английский

Citations

1
Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults DOI Creative Commons
Zhenyou Wang, Xiaoquan Cai, Ao Luo

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 7906 - 7906

Published: Dec. 11, 2024

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The error converges to bounds in by an improved function and coordinate transformation. Due unknown faults sensors, system states cannot be gained correctly; therefore, adaptive compensation strategy is constructed based on approximation capabilities of neural networks solve negative impact failures. reinforcement-learning-based backstepping proposed realize system. Utilizing Lyapunov stability theory, it shown that designed controller enables converge fixed time all signals closed-loop are bounded probability. Finally, simulation results prove effectiveness method.

Language: Английский

Citations

0