Optimal Learning Control for Nonlinear Faulty Systems With Time-Varying Trial Lengths
针对执行器故障和时变试验长度的非线性离散系统，提出一种间歇最优学习控制策略，通过搜索最近可用试验来最小化跟踪误差和输入漂移，理论证明跟踪误差渐近收敛且对初始状态偏移具有鲁棒性。
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.