基于改进灰色预测模糊PID控制的列车多目标优化研究

针对列车自动驾驶(ATO)系统各性能指标最优问题,充分考虑灰色预测控制、模糊控制与PID控制各自的优点,提出一种改进灰色预测模糊PID控制算法。以准时性、舒适性、精准停车及能耗为指标,列车动力学方程为约束,构建列车运行多目标模型;然后采用遗传算法优化该模型,根据MATLAB软件得到列车运行目标曲线;最后利用Simulink模块搭建PID控制器仿真模型、模糊PID控制器仿真模型和改进灰色预测模糊PID控制器仿真模型,获得其对应的跟踪曲线。选用车型和线路仿真模拟,仿真结果表明:改进灰色预测模糊PID控制算法比PID控制算法和模糊PID控制算法在提高列车运行的准时性、舒适性、停车精确性以及降低能耗方面更有效。

A Study on Multi-Objective Train Optimization Based on Improved Grey Prediction Fuzzy PID Control

Aiming at the optimization of each performance index of automatic train operation(ATO) system, and considering fully the individual advantages of grey predictive control, fuzzy control and PID control, this paper tries to propose an improved grey prediction fuzzy PID control algorithm. The multi-objective model is built based on the indexes of punctuality, comfort, accurate parking and energy consumption and the constraint of train dynamics equation. Then, genetic algorithm is used to optimize the mode, and train operation target curve is obtained according to MATLAB simulation software. Finally, the Simulink module is used to build the PID controller simulation model, the fuzzy PID controller simulation model and the improved grey prediction fuzzy PID controller simulation model to obtain the corresponding train tracking curves. The simulation of the selected train model and line verifies that the improved grey predictive fuzzy PID control algorithm is more effective than the PID control algorithm and the fuzzy PID control algorithm in improving punctuality, comfort, accurate parking and reducing energy consumption of train operation.