Automatic Train Operation Algorithm Based on Adaptive Iterative Learning Control Theory

To study the automatic control of high-speed trains with time-varying exterior disturbances and state saturation, this paper proposes an adaptive iterative learning control algorithm. Based on Lyapunov function, the control law and the parameter of updating law are deduced by considering the state error during the operating process. Then the Lyapunov-like composite energy function is established. The differential negative definiteness and robustness of the proposed function are verified. The proposed adaptive iterative learning control algorithm has been applied to computational simulation and real case study to verify the tracking performance. The results show that the proposed algorithm improves tracking accuracy and convergence speed. It was able to accurately track the desired profile with less iterative times than before.