共 36 条
Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm
被引:8
作者:
Liu, Gang
[1
,2
,3
]
Xu, Chuanfang
[4
]
Wang, Longda
[4
]
机构:
[1] Inner Mongolia Minzu Univ, Coll Engn, Tongliao 028000, Peoples R China
[2] Jiangxi New Energy Technol Inst, Sch Mech & Elect Engn, Xinyu 338004, Peoples R China
[3] Shanghai Jiao Tong Univ, Dept Automation, Shanghai 200240, Peoples R China
[4] Dalian Jiaotong Univ, Sch Automation & Elect Engn, Dalian 116026, Peoples R China
来源:
关键词:
permanent magnet synchronous motor;
auto disturbance rejection control;
improved memetic algorithm;
optimal control function;
Gaussian mutation;
fusion distance;
PMSM;
CONTROLLER;
D O I:
10.3390/s23073621
中图分类号:
O65 [分析化学];
学科分类号:
070302 ;
081704 ;
摘要:
In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth-flame optimization.
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页数:20
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