Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks | Zendy

Tat-Bao-Thien Nguyen | Zendy; TehLu Liao | Zendy; JunJuh Yan | Zendy

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Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

Author(s) -

Tat-Bao-Thien Nguyen,

TehLu Liao,

JunJuh Yan

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/868415

Subject(s) - control theory (sociology) , controller (irrigation) , artificial neural network , fuzzy logic , lyapunov stability , chaotic , nonlinear system , computer science , sliding mode control , adaptive control , control engineering , mode (computer interface) , lyapunov function , engineering , control (management) , artificial intelligence , physics , quantum mechanics , agronomy , biology , operating system

In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method

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