基于GA优化模糊PID控制的ATO算法研究

摘要

Aiming at the poor adaptability and lack of intelligence of the traditional PID control method of the urban rail train’s automatic train operation ( ATO) system, the fuzzy PID control algorithm optimized by genetic algorithm is applied to the control of ATO based on the operation experiences of experts and drivers in this field, and the simulation is carried out by using MTALAB. Simulation results show that this algorithm is better than traditional PID control, allowing ATO system to satisfy requirements of adaptability and intelligence in different working conditions, achieve precise stop and punctual arrival, improve ride comfort and reduce effectively energy consumption of train.%针对城市轨道列车的自动驾驶系统( ATO)传统PID控制方法适应性差和智能化不足的问题，基于该领域专家知识和驾驶司机的操作经验，将遗传算法优化的模糊 PID 控制算法运用在 ATO 的控制系统中，并运用MTALAB进行仿真。仿真结果表明，该控制算法优于传统的PID控制，能够满足ATO系统对不同工况下的适应性和智能性要求，可以达到精确停车和准点到站的目的，能够有效提高列车舒适性和降低列车能耗。