基于蚁狮优化算法分数阶PI的PMSM矢量控制

Vector Control of PMSM Based on Ant Lion Optimization Algorithm of Fractional-order PI

针对永磁同步电机(PMSM)双闭环矢量控制系统中传统工程经验比例积分(PI)参数整定易出现超调、振荡、鲁棒性不强等问题,提出采用分数阶PI(FOPI)控制器应用于系统的转速外环,并利用蚁狮优化(ALO)算法对FOPI控制器进行参数优化。该算法利用自然界中蚁狮狩猎蚂蚁的过程,将待优化的参数看作蚁狮个体所处空间位置,并利用误差性能指标ITAE作为目标函数,搜索待求参数的全局最优解。为满足电机驱动控制的实时性要求,采用ALO算法离线整定待求参数。实验结果表明,ALO算法优化的转速外环FOPI控制器具有减小调节时间、提高响应速度和增强系统抗干扰能力的优势,证明了优化策略的可行性和有效性。

Aiming at the problems of traditional engineering experience proportional integral(PI)parameter setting in permanent magnet synchronous motor(PMSM)double closed-loop vector control system,which are prone to overshoot,oscillation,and weak robustness,a fractional-order PI(FOPI)controller is applied to the outer speed of the system.The ant lion optimization(ALO)algorithm optimizes the parameters of the FOPI controller.This algorithm uses the process of hunting ants by ant lions in nature,regards the parameters to be optimized as the spatial position of the individual ant lions,and uses the error performance index ITAE as the objective function to search for the global optimal solution of the parameters to be sought.In order to meet the real-time requirements of motor drive control,ALO algorithm is used to tune the parameters to be sought offline.The experimental results show that the outer speed FOPI controller optimized by ALO algorithm has the advantages of reducing adjustment time,improving response speed,and enhancing the anti-interference ability of the system,which proves the feasibility and effectiveness of the optimization strategy.