An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances

An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances

Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence,...

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Bibliographic Details
Main Authors: Shengquan Li, Juan Li, Yanqiu Shi
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/4034320
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spelling doaj-0615f26f50b54e9abe7ddab1613c20092020-11-24T21:50:58ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/40343204034320An RBFNN-Based Direct Inverse Controller for PMSM with DisturbancesShengquan Li0Juan Li1Yanqiu Shi2School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaSchool of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaSchool of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaConsidering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN-based direct inverse control method.http://dx.doi.org/10.1155/2018/4034320
collection DOAJ
language English
format Article
sources DOAJ
author Shengquan Li
Juan Li
Yanqiu Shi
spellingShingle Shengquan Li
Juan Li
Yanqiu Shi
An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
Complexity
author_facet Shengquan Li
Juan Li
Yanqiu Shi
author_sort Shengquan Li
title An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
title_short An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
title_full An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
title_fullStr An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
title_full_unstemmed An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
title_sort rbfnn-based direct inverse controller for pmsm with disturbances
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2018-01-01
description Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN-based direct inverse control method.
url http://dx.doi.org/10.1155/2018/4034320
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AT juanli anrbfnnbaseddirectinversecontrollerforpmsmwithdisturbances
AT yanqiushi anrbfnnbaseddirectinversecontrollerforpmsmwithdisturbances
AT shengquanli rbfnnbaseddirectinversecontrollerforpmsmwithdisturbances
AT juanli rbfnnbaseddirectinversecontrollerforpmsmwithdisturbances
AT yanqiushi rbfnnbaseddirectinversecontrollerforpmsmwithdisturbances
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