Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton-Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.