GWO-RBFNN Dual-parameter Collaborative Intelligent Optimal Control of Chaotic Motion of a Class of Permanent Magnet Synchronous Motor
李宁洲 1邱思旋 1卫晓娟 1李小齐 1李高嵩1
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作者信息
1. 上海应用技术大学 轨道交通学院,上海 201418
折叠
摘要
针对永磁同步电机(permanent magnet synchronous motor,PMSM)混沌控制问题,提出了一种基于GWO-RBFNN的双参协同智能优化控制方法.从控制器能够自动搜索预期运动状态的角度出发,选择Poincaré截面上两点间距离作为控制器输入,并考虑到系统参数对系统动力学行为的耦合影响作用,基于径向基函数神经网络(radial basis function neural network,RBFNN)设计了双参协同控制器;采用灰狼优化算法(grey wolf optimization,GWO)优化选择控制器参数,以实现最佳的控制器性能;通过对PMSM系统中两个可控参数进行微幅扰动调整,将系统从混沌状态控制到预期的运动状态.研究结果表明,相较于基于GWO-RBFNN的单参数智能优化控制方法,基于GWO-RBFNN的双参协同智能优化控制方法具有更优的性能.虽然两种方法均能实现混沌运动控制,但相较而言,基于GWO-RBFNN的双参协同智能优化控制方法控制速度更快,超调量更小.
Abstract
Aiming at the chaos control of permanent magnet synchronous motor,a dual-parameter collaborative intelligent optimal control method based on GWO-RBFNN was proposed.Starting from the perspective that the controller can automatically search for the expected motion state,the distance between two points on the Poincaré cross section is selected as the controller input.And considering the coupling effect of system parameters on the dynamic behavior of the system,a dual-parameter cooperative controller is designed based on radial basis function neural network(RBFNN);Grey Wolf Optimization(GWO)algorithm is used to optimize and select controller parameters to achieve the best controller performance;The system is controlled from a chaotic state to the expected motion state by adjusting the two controllable parameters in the PMSM system with minor disturbances.The results show that compared with the single-parameter intelligent optimization control method based on GWO-RBFNN,the dual-parameter collaborative intelligent optimal control method based on GWO-RBFNN has better performance.Although both methods can achieve chaotic motion control,the control speed of the dual-parameter collaborative intelligent optimization control method based on GWO-RBFNN is faster and overshoot is smaller.
关键词
永磁同步电机/混沌运动/双参协同控制/灰狼算法/径向基函数神经网络
Key words
permanent magnet synchronous motor/chaotic motion/dual-parameter cooperative control/grey wolf optimization/radial basis function neural network
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