Data-based fault tolerant control for affine nonlinear systems through particle swarm optimized neural networks

In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.