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

Shaohua Luo | Zendy; Jiaxu Wang | Zendy; Zhen Shi | Zendy; Qian Qiu | Zendy

Open Access

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

Author(s) -

Shaohua Luo,

Jiaxu Wang,

Zhen Shi,

Qian Qiu

Publication year - 2014

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2014/315634

Subject(s) - control theory (sociology) , computer science , controller (irrigation) , artificial neural network , basis (linear algebra) , surface (topology) , permanent magnet synchronous motor , adaptive control , stability (learning theory) , function (biology) , tracking (education) , control (management) , magnet , mathematics , engineering , artificial intelligence , mechanical engineering , geometry , machine learning , evolutionary biology , agronomy , biology , psychology , pedagogy

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

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