摘要
为了提高感应电机磁场定向控制(FOC)系统中的速度控制器性能,提出一种基于简化粒子群优化(SPSO)算法的PID参数自整定方法,并应用到速度控制器中。在传统PSO算法中去掉了粒子速度参数,并融入了动态惯性权重,构建了一种SPSO算法。将PID控制器的3个参数编码为粒子位置,将电机速度的平方误差积分(ISE)作为适应度函数,通过SPSO算法获得最优的PID参数。根据最优参数构建FOC中的速度控制器,以实现电机的高效控制。仿真结果表明,提出的SPSO-PID控制器能够快速且稳定地控制电机速度到设定值,且对负载变化具有鲁棒性。
In order to improve the speed controller performance in induction motor field oriented control （FOC） sys- tem, a PID parameter self-tuning method based on simplified particle swarm optimization （SPSO） algorithm was proposed and applied to the speed controller. The particle velocity parameter in the traditional PSO algorithm was removed, and in- tegrate the dynamic inertia weight to construct an SPSO algorithm. The three parameters of the PID controller were encoded into the particle position, and the quadratic error integral （ISE） of the motor speed was taken as the fitness function, the SPSO algorithm was used to optimize PID parameters. The speed controller in FOC was constructed according to the optimal parameters, and the efficient control of the motor was realized. The simulation results show that the proposed SPSO-PID controller can quickly and stably control the motor speed to the set value, and it is robust to the load variation.
出处
《微特电机》
北大核心
2017年第7期59-62,67,共5页
Small & Special Electrical Machines
基金
国家自然科学基金项目(61540066)
河南省教育厅科学技术研究重点项目(13A520221
14A520045)
贵州省重大基础研究项目(黔科合JZ字[2014]2001号)
关键词
感应电机
磁场定向控制
简化粒子群优化
动态惯性权重
PID参数自整定
induction motor
field orientated vector control （ FOC ）
simplified particle swarm optimization （ SPSO ）
dynamic inertia weight
self-tuning of PID parameters
