Precise robust adaptive dynamic surface control of permanent magnet synchronous motor based on extended state observer

2017 ◽  
Vol 11 (5) ◽  
pp. 590-599 ◽  
Author(s):  
Guotao Li ◽  
Wenfu Xu ◽  
Jianguo Zhao ◽  
Shuai Wang ◽  
Bing Li
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Dynamic Surface Control ◽  
Synchronous Motor ◽  
Extended State Observer ◽  
Extended State ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Surface Control ◽  
Robust Adaptive

scholarly journals Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Shaohua Luo ◽  
Jiaxu Wang ◽  
Zhen Shi ◽  
Qian Qiu
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Time Delays ◽  
Permanent Magnet Synchronous Motor ◽  
Dynamic Surface Control ◽  
Synchronous Motor ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Surface Control ◽  
Uncertain Time

This paper focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed control has been illustrated through simulation results.

scholarly journals Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shaohua Luo
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Nonlinear Dynamic ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Dynamic Surface Control ◽  
Synchronous Motor ◽  
Unknown Parameters ◽  
Dynamic Surface ◽  
Surface Control

This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.

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