Adaptive Reinforcement Learning for Fault-Tolerant Optimal Consensus Control of Nonlinear Canonical Multiagent Systems With Actuator Loss of Effectiveness
Abstract
This article addresses the adaptive optimized consensus tracking control problem of nonlinear multiagent systems (MASs) via a reinforcement learning (RL) algorithm. Specifically, the nonlinear high-order MASs are formulated in a canonical form, with considerations for both actuator effectiveness loss and time-varying bias faults. First, neural networks (NNs) are utilized to approximate unknown nonlinear dynamics, and a state identifier and a fault estimator based on NNs are established, both of which are essential for evaluating state information and bias faults, respectively. Second, to achieve a high-order canonical dynamic consensus and enhance the efficiency of the consensus control strategy, a sliding-mode mechanism is employed to regulate tracking errors. Moreover, we develop an adaptive NN-based fault-tolerant optimal control method by integrating the sliding-mode mechanism with an actor–critic structured RL algorithm. It is proved that the outputs of the MASs precisely align with the desired reference signals, while ensuring the boundedness of all closed-loop signals. Finally, the proposed control methodology's effectiveness is validated through a simulation example.