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.