Optimal Learning Control for Nonlinear Faulty Systems With Time-Varying Trial Lengths

This article proposes an intermittent optimal learning control strategy for nonlinear discrete-time systems under time-varying pass lengths and actuator faults. The target of the problem is to minimize the timewise tracking error and the input drifts, which are combined by a time-iteration-dependent factor. By searching the nearest available pass at each time instant for the current iteration, the optimal control gain can be obtained. Theoretical analysis indicates that the tracking error converges asymptotically in spite of the actuator fault and the robustness against the shifted initial state is further proven. Numerical simulations illustrate the effectiveness and robustness of the presented method.