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

In this paper, a data-based fault tolerant control &#x0028 FTC &#x0029 scheme is investigated for unknown continuous-time &#x0028 CT &#x0029 affine nonlinear systems with actuator faults. First, a neural network &#x0028 NN &#x0029 identifier based on particle swarm optimization &#x0028 PSO &#x0029 is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network &#x0028 PSOCNN &#x0029 is employed to solve the Hamilton-Jacobi-Bellman equation &#x0028 HJBE &#x0029 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.