Adaptive Iterative Learning Control for High-Speed Trains With Unknown Speed Delays and Input Saturations

摘要

In this paper, an adaptive iterative learning control (AILC) strategy for high-speed trains with unknown speed delays and control input saturations is designed to address speed trajectory tracking problem. The train motion dynamics containing nonlinearities and parametric uncertainties are formulated as a nonlinearly parameterized system. Instead of estimation or modeling of train delays, an unknown time-varying delay term is integrated into the speed on delay analysis by means of Lyapunov–Krasovskii function. Through rigorous analysis, it is confirmed that the proposed AILC mechanism can guarantee convergence of train speed to the desired profile during operations repeatedly. Case studies with numerical simulations further verify the effectiveness of the proposed approach.