基于PID型迭代学习控制的列车自动驾驶曲线跟踪算法研究

Curve tracking algorithm for automatic train operation based on PID-type iterative learning control

针对基于比例微分积分(PID,Proportional Integral Derivative)控制的列车速度跟踪算法在跟踪进度、收敛性和稳定性等方面存在的不足,提出一种基于PID型迭代学习控制(ILC,Iterative Learning Control)的列车自动驾驶(ATO,Automatic Train Operation)曲线跟踪算法。通过迭代学习控制,优化跟踪过程,减小跟踪误差,缩短收敛时间;设置典型场景对所设计的算法进行仿真试验,并将仿真结果与基于PID控制算法的跟踪效果进行对比分析。结果表明,PID型ILC算法对列车目标速度和目标位移具有较高的跟踪精度,能够在有限的迭代次数内实现精确跟踪,验证了所提算法的有效性。

To address the shortcomings of train speed tracking algorithms based on proportional integral derivative(PID) control in tracking progress,convergence,and stability,this paper oriposed a PID based iterative learning control(ILC) based automatic train operation(ATO) curve tracking algorithm.The paper optimized the tracking process through iterative learning control,reduced tracking errors,and shorten convergence time,set up typical scenarios to conduct simulation experiments on the designed algorithm,and compared and analyzed the simulation results with the tracking effect of the PID control algorithm.The results show that the PID ILC algorithm has high tracking accuracy for train target speed and target displacement,and can achieve precise tracking within a limited number of iterations,proving the effectiveness of the proposed algorithm.