Tracking Control via Iterative Learning for High-Speed Trains With Distributed Input Constraints

This paper investigates the tracking control problem of high-speed trains in the presence of the input constraints caused by the distribution and output capacity of power systems. By virtue of the repetitive operation pattern of trains and the backstepping technique, an adaptive iterative learning control (ILC) strategy based on the multi-particle model is proposed to drive the train to track the given reference displacement and velocity, where the unknown time-varying parameters are learned and adjusted between successive operations, and an input-dependent auxiliary system is introduced to compensate the influence of input constraints. During the design of the controller, the Lyapunov function and composite energy function (CEF) are constructed to ensure the stability of the closed-loop system and the convergence of tracking errors for high-speed trains. Furthermore, numerical simulation is performed to confirm the effectiveness of the proposed scheme. The three main contributions of this work lie in: 1) Integrating the multi-particle model and ILC framework, which can more accurately reveal the dynamics of the train, and take full advantage of the repetitive operation pattern; 2) Following the backstepping procedure to devise the learning controller, where the parameter uncertainties and modeling inaccuracies are deliberately handled, and; 3) Solving the issues of distributed input constraints for the control system of high-speed trains.