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

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 L [0, T] 2 convergence of train speed to the desired profile during operations repeatedly. Case studies with numerical simulations further verify the effectiveness of the proposed approach.