Computationally Inexpensive Robust Data Driven Optimal Point-To-Point Tracking ILC for City Subway Trains Subject to Iteration-Dependent Disturbances

This paper presents a robust data driven optimal point-to-point ILC for subway trains with multiple-point tracking and subject to iteration-dependent disturbances by only utilizing input output data of the train system. Firstly, the tracking task requires that the control input is updated according to the prespecified measured multiple-point tracking error values rather than the complete output trajectory, which can reduce computational cost. Secondly, without model information of the train system, a robust data driven control law is designed. Then, rigorous analysis is developed which demonstrates that the train tracking error is monotonic uniformly ultimately bounded convergence and the ultimate bound which only depends on the disturbances boundedness. Finally, a simulation is conducted for train system to verify the effectiveness of theoretical studies.