Adaptive fault-tolerant control for affine non-linear systems based on approximate dynamic programming

This study investigates the fault-tolerant control problem for affine nonlinear systems with time-varying actuator gain and bias faults. In order to handle the actuator faults and guarantee the approximate optimal performance of the nominal non-linear dynamics, the approximate dynamic programming method is used to design a sliding mode fault-tolerant control policy. First, the actuator faults are estimated using a disturbance observer and a novel adaptive scheme. Based on the fault estimations, an integral sliding function is constructed and the reachability condition is derived. Then, an actor-critic algorithm with new weight tuning laws is given to learn the bounded nearly optimal control policy for the nominal dynamics. The convergence of the neural network weights is presented based on a Lyapunov analysis method. Finally, the simulation results are given to verify the efficacy of the developed method.