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Direct adaptive neural control of chaos in the permanent magnet synchronous motor.pdf

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Direct adaptive neural control of chaos in the permanent magnet synchronous motor

Nonlinear Dyn (2012) 70:1879–1887 DOI 10.1007/s11071-012-0580-2 O R I G I NA L PA P E R Direct adaptive neural control of chaos in the permanent magnet synchronous motor Jinpeng Yu · Haisheng Yu · Bing Chen · Junwei Gao · Yong Qin Received: 16 May 2012 / Accepted: 13 August 2012 / Published online: 4 September 2012 ? Springer Science+Business Media B.V. 2012 Abstract In this paper, a direct adaptive neural speed tracking control is addressed for the chaotic permanent magnet synchronous motor (PMSM) drive systems via backstepping. Neural networks are directly used to ap- proximate unknown and desired control signals and a novel direct adaptive tracking controller is constructed via backstepping. The proposed adaptive neural con- trollers guarantee that the tracking error converges to a small neighborhood of the origin. Compared with the conventional backstepping method, the designed neu- ral controller’s structure is very simple. Simulation re- sults show that the proposed control scheme can sup- press the chaos of PMSM and guarantees the perfect tracking performance even with the existence of un- known parameters. Keywords Chaos control · Nonlinear system · Neural networks · Adaptive control · Permanent magnet synchronous motor · Backstepping J. Yu () · H. Yu · B. Chen · J. Gao The College of Automation Engineering, Qingdao University, Qingdao 266071, P.R. China e-mail: yjp1109@ J. Yu · Y. Qin State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P.R. China 1 Introduction Permanent magnet synchronous motors (PMSM) are of great interest for industrial applications due to their high speed, high efficiency, high power density, and large torque to inertia ratio. The secure and stable operation of the PMSM, which is an essential re- quirement of industrial automation manufacturing, has received considerable attention because its dynamic model is nonlinear, multivariable and even experienc- ing Hopf bifurcation, limit cycle