Abstract: Vehicle return to center control is an important factor that determines the quality of the steering system. Aiming at the problems of under-alignment or overshoot in new-energy high-torque commercial vehicles, a fuzzy proportional-integral-differential (PID) control algorithm based on multi-population genetic algorithm (MPGA), optimization is proposed. The electric power steering (EPS) system dynamic model, steering wheel angle estimation model and back-to-center compensation control strategy are established. Then determine the armature voltage of the motor according to the expected output torque, so as to realize the return compensation. The parameters of fuzzy PID controller are optimized by using MPGA. The Simulink simulation test proves that the proposed fuzzy PID control algorithm based on MPGA optimization, compared with the PID control algorithm and fuzzy PID control algorithm, has improved return performance.

Key words: EPS; Return to center control; Fuzzy PID control; MPGA

摘要： 车辆回正控制是决定转向系统好坏的一个重要因素。针对新能源大扭矩商用车存在回 正不足或回正超调问题，文章提出了基于多种群遗传算法（MPGA）优化的模糊比例-积分- 微分（PID）控制算法，建立了电动助力转向（EPS）系统动力学模型、方向盘转角估计模型 与回正补偿控制策略，根据估计的方向盘转角确定电机的期望输出扭矩，以此确定电机电枢 电压，从而实现回正补偿。利用 MPGA 优秀的全局寻优性能，对模糊 PID 控制器参数进行优 化。经 Simulink 仿真试验证明，提出的基于 MPGA 优化的模糊 PID 控制算法，相较于 PID 控制与模糊 PID 控制，回正性能得到改善。

关键词: EPS；回正控制；模糊 PID 控制；MPGA