Robust Adaptive Iterative Learning Control for Multitrain System With Actuator Saturation and State Constraints

In this article, the cooperative control issue is investigated for multitrain system with actuator saturation and state constraints. Considering the uncertainty of the high-speed train dynamics model and the nonstrict repetition of the operating process, a robust adaptive iterative learning control (RAILC) method is proposed for multitrain system cooperative control, where a control-based nonlinear auxiliary system is introduced to realize the finite-time compensation of train actuator saturation. Furthermore, a novel barrier RAILC (BRAILC) method is developed to achieve actively speed and position state constraints, which can ensure the multitrain system maintaining operates within the safe range. As the best of the author’s knowledge, this is the first time that the robust control and adaptive iterative learning control (AILC) are applied in a multitrain system. The Lyapunov function and composite energy function (CEF) are used to analyze the system convergence along the time and iteration axis, respectively. In the time axis of each iteration, the speed and position tracking errors of multiple high-speed train are convergent, and the system state is constrained, thus the proposed controller is reliable in engineering. Furthermore, through iterative learning law, the speed and position tracking error will converge to zero along the iteration axis, which gives the system the ability to intelligently utilize historical data. Finally, the effectiveness of the proposed control method is demonstrated through numerical simulations using data from China railway high-speed 380B (CRH380B) train.