基于PSO优化的列车制动模糊PID控制算法

Fuzzy PID Control Algorithm for Train Braking based on PSO Optimization

根据城轨列车制动模型设计基于粒子群优化(PSO)算法的自适应模糊PID控制器,以模糊PID的量化因子和比例因子为优化对象,ITAE(时间误差绝对值积分)指标做为优化算法的目标函数,建立以停车精度和舒适度为目标的控制策略,对列车进行停车控制.为了说明算法的有效性,选择基于前馈补偿的PID控制算法做为对比,在Matlab/Simulink环境下建立制动仿真模型,结果表明:优化后的自适应模糊PID算法各项指标均优于前馈补偿PID算法.

According to urban rail train braking model design,the train parking control was conducted based on particle swarm optimization(PSO)algorithm of adaptive fuzzy PID controller,fuzzy PID quantization factor and scaling factor as optimization object,ITAE(the time integral of absolute value of error)index as the objective function of optimization algorithm based on parking accuracy and comfort as the goal of control strategy.In order to illustrate the effectiveness of the algorithm,the PID control algorithm based on feedforward compensation was selected as a comparison,and the braking simulation model was established in Matlab/Simulink environment.The results show that the optimized adaptive fuzzy PID algorithm is superior to the feedforward compensation PID algorithm in all indexes.