Event-triggered decentralized optimal fault tolerant control for mismatched interconnected nonlinear systems through adaptive dynamic programming

In this paper, we propose an event-triggered decentralized optimal fault tolerant control (ETDOFTC) scheme based on adaptive dynamic programming for mismatched interconnected nonlinear systems with actuator failures. For fault-free dynamic models, the decentralized control problem is addressed by developing a set of decentralized optimal control strategies for isolated subsystems with modified value functions, which are approximated by critic neural networks. Meanwhile, the neural network-based decentralized observer is established to approximate actuator failures and mismatched unknown interconnections. The weights of the neural networks are aperiodically updated at the designed triggering instants. Then, the proposed ETDOFTC scheme is obtained by combining the event-triggered decentralized optimal control strategies with the adaptive fault compensators. Furthermore, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded via the Lyapunov stability analysis. Finally, simulation results of two examples are presented to confirm the effectiveness of the proposed ETDOFTC scheme.