ADP-Based Fault-Tolerant Control for Multiagent Systems With Semi-Markovian Jump Parameters
针对存在执行器偏置故障的半马尔可夫跳变多智能体系统，提出一种融合自适应动态规划与容错控制的数据驱动方案，实现在线调整和自动补偿，保证系统信号有界且跟随者与领导者状态一致。
This article analyzes and validates an approach of integration of adaptive dynamic programming (ADP) and adaptive fault-tolerant control (FTC) technique to address the consensus control problem for semi-Markovian jump multiagent systems having actuator bias faults. A semi-Markovian process, a more versatile stochastic process, is employed to characterize the parameter variations that arise from the intricacies of the environment. The reliance on accurate knowledge of system dynamics is overcome through the utilization of an actor-critic neural network structure within the ADP algorithm. A data-driven FTC scheme is introduced, which enables online adjustment and automatic compensation of actuator bias faults. It has been demonstrated that the signals generated by the controlled system exhibit uniform boundedness. Additionally, the followers' states can achieve and maintain consensus with that of the leader. Ultimately, the simulation results are given to demonstrate the efficacy of the designed theoretical findings.