基于自适应迭代学习控制的列车自动驾驶算法

针对高速列车自动驾驶系统受到时变外部扰动和受限状态的情况，提出一种基于迭代学习控制的自适应控制算法. 基于Lyapunov 函数，利用列车运行过程中的状态偏差，推导出自适应迭代学习控制律和参数学习更新律. 构造类Lyapunov 函数的复合能量函数，通过迭代域的差分，证明其差分负定性和收敛性. 采用所提控制算法对列车跟踪性能进行计算机仿真和实例仿真验证，结果表明，所提出的自适应迭代学习控制算法对列车期望曲线跟踪具有较高的精度和较快的收敛速度，能够在较短的迭代次数实现对期望曲线的精确跟踪.

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.