Event-triggered-based fixed-time adaptive neural fault-tolerant control for stochastic nonlinear systems under actuator and sensor faults

In this paper, the problem of adaptive fault-tolerant control with event-triggered scheme is studied for a class of uncertain stochastic nonlinear systems with the actuator and sensor faults including the additional faults and multiplicative faults. For the controlled system with unknown nonlinear functions, the neural networks are employed to reestablish the system model. Based on the event-triggered mechanism, a novel adaptive fixed-time control strategy with the fault state is proposed by using the backstepping technique. Under the developed adaptive controller, all the closed-loop system signals are bounded in probability in a fixed time, and the convergence time is irrelevant to the initial states of the system. The practicability of the designed controller is illustrated by two simulation examples.