Adaptive robust speed control based on recurrent elman neural network for sensorless PMSM servo drives

被引：48
作者：
Jon, Ryongho [1 ,2 ]
Wang, Zhanshan [1 ]
Luo, Chaomin [3 ]
Jong, Myongguk [2 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China
[2] Natl Acad Sci, Controlled Machine Inst, Pyongyang, North Korea
[3] Univ Detroit Mercy, Dept Elect & Comp Engn, Detroit, MI 48221 USA
来源：
NEUROCOMPUTING | 2017年 / 227卷
基金：
中国国家自然科学基金;
关键词：
Permanent magnet synchronous motor (PMSM); Recurrent Elman neural network (RENN); Adaptive; Robust; Reconstruction error; Learning rate; TRACKING CONTROL; SYSTEM; MODE; DESIGN;
D O I：
10.1016/j.neucom.2016.09.095
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an adaptive robust control scheme based on recurrent Elman neural network (RENN) is proposed to achieve high-performance speed tracking despite of the existence of system uncertainties for the sensorless permanent magnet synchronous motor (PMSM) servo drive. Firstly, the dynamics of sensorless PMSM operated with the system uncertainties are described in details. Secondly, an adaptive RENN speed controller (ARENNSC) composed of an RENN controller and a compensated controller is developed to achieve the adaptive robust speed control of PMSM drive. The RENN controller is designed to imitate an ideal speed control signal for sensorless PMSM, and the compensated controller is designed to compensate an error between ideal control signal and actual RENN signal, including an RENN reconstruction error. The adaptive laws are derived based on Lyapunov theorem to ensure the stability of ARENNSC. Then, a calculation method of ideal learning rate is also presented to improve the adaptive performance of ARENNSC. The simulation results demonstrate the feasibility, robustness and good dynamic performance of the proposed adaptive RENN speed control scheme.
引用
收藏
页码：131 / 141
页数：11
相关论文
共 42 条
[1]  
Astrom K.J., 1995, ADAPTIVE CONTROL
[2]   An approach to sensorless operation of the permanent-magnet synchronous motor using diagonally recurrent neural networks [J].
Batzel, TD ;
Lee, KY .
IEEE TRANSACTIONS ON ENERGY CONVERSION, 2003, 18 (01) :100-106
[3]   Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position [J].
Bolognani, S ;
Oboe, R ;
Zigliotto, M .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 1999, 46 (01) :184-191
[4]   Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines [J].
Chaoui, Hicham ;
Sicard, Pierre .
NEURAL COMPUTING & APPLICATIONS, 2011, 20 (05) :717-727
[5]   Sliding-Mode Sensorless Control of Direct-Drive PM Synchronous Motors for Washing Machine Applications [J].
Chi, Song ;
Zhang, Zheng ;
Xu, Longya .
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2009, 45 (02) :582-590
[6]   A Discrete-Time VS Controller based on RBF Neural Networks for PMSM Drives [J].
Ciabattoni, Lucio ;
Corradini, Maria Letizia ;
Grisostomi, Massimo ;
Ippoliti, Gianluca ;
Longhi, Sauro ;
Orlando, Giuseppe .
ASIAN JOURNAL OF CONTROL, 2014, 16 (02) :396-408
[7]   Optimal Tracking Control of Affine Nonlinear Discrete-time Systems with Unknown Internal Dynamics [J].
Dierks, Travis ;
Jagannathan, S. .
PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009), 2009, :6750-6755
[8]   Intelligent Optimal Recurrent Wavelet Elman Neural Network Control System for Permanent-Magnet Synchronous Motor Servo Drive [J].
El-Sousy, Fayez F. M. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (04) :1986-2003
[9]   Hybrid H∞-Based Wavelet-Neural-Network Tracking Control for Permanent-Magnet Synchronous Motor Servo Drives [J].
El-Sousy, Fayez F. M. .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2010, 57 (09) :3157-3166
[10]   FINDING STRUCTURE IN TIME [J].
ELMAN, JL .
COGNITIVE SCIENCE, 1990, 14 (02) :179-211
12345