基于SSA优化的永磁同步电机级联模型预测控制研究
Predictive Control Study of Cascade Model for Permanent Magnet Synchronous Motors Based on SSA Optimization
蔡宏越 1马家庆 1何志琴 1吴钦木 1陈昌盛 1覃涛1
作者信息
- 1. 贵州大学电气工程学院,贵阳 550025
- 折叠
摘要
针对永磁同步电机(permanent magnet synchronous motor)使用传统PI控制鲁棒性差和动态响应慢,以及传统有限集电流预测控制方法控制稳定性差和电流脉动较大的问题,提出了基于麻雀搜索算法(sparrow search algorithm,SSA)优化的级联模型预测控制方法.首先,将速度外环采用模型预测速度控制器,利用麻雀搜索算法来在线整定控制器参数,该方法可以快速找到最优的控制器参数,使其更加准确地控制电机运转;其次,电流内环采用最优占空比模型预测电流控制器,使得电流脉动减小;最后,通过仿真对比实验结果表明所提出的控制方法显著地降低了超调量,快速响应性能更好、抗干扰能力更优,同时可以有效抑制电流脉动,证明了所提方法的合理性和有效性.
Abstract
Aiming at the problems that the poor robustness and slow dynamic response of the traditional PI control of permanent magnet synchronous motor,as well as poor control stability and large current pulsation of the traditional finite set current prediction control method,a cascade model predictive control method based on the optimization of the sparrow search algorithm is proposed.Firstly,the speed loop uses a model predictive speed controller,using a sparrow search algorithm to adjust the controller parameters online.This method allows the optimal controller parameters to be found quickly,making it possible to control the motor operation more accurately.Secondly,the current loop uses the optimal duty cycle model to predict the cur-rent controller,which reduces the current pulsation.Finally,simulation and comparison experimental results show that the proposed control method significantly reduces overshoot,has better fast response performance and better anti-interference capability,and can effectively suppress current pulsation,which proves the ra-tionality and effectiveness of the proposed method.
关键词
永磁同步电机/麻雀搜索算法/模型预测控制/最优占空比/级联Key words
permanent magnet synchronous motor/sparrow search algorithm/model predictive control/op-timal duty cycle/cascade引用本文复制引用
基金项目
国家自然科学基金资助项目(62163006)
贵州省科技计划项目(黔科合支撑[2021]一般442)
贵州省科技计划项目([2022]一般264)
贵州省科技计划项目([2023]一般096)
贵州省科技计划项目([2023]一般179)
出版年
2024
NETL
NSTL
万方数据