永磁直线同步电机的智能互补滑模控制

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Abstract Intelligent complementary sliding mode control(ICSMC) based on radial basis function(RBF) neural network is proposed for position tracking precision in permanent magnet linear synchronous motor(PMLSM) servo system.Firstly,PMLSM dynamic equation which contains uncertainty factors such as the end effect,parameter variations,external disturbances and nonlinear friction is established.Then,complementary sliding mode controller is designed by adopting the combination of the generalized sliding surface and the complementary sliding surface in order to reduce the system tracking error,improve response speed and weak the chattering.RBF neural network is employed to estimate the system uncertainty directly.Moreover,RBF network parameters which are trained online can improve the system dynamic performance and robustness.Furthermore,the Lyapunov theorem is used to guarantee the close-loop system stability.By analyzing the system experiment results, it is proved that the proposed control method effectively reduces the system tracking error, and makes the system have good dynamic performance and robust performance.

Key words： Permanent magnet linear synchronous motor complementary sliding mode control radial basis function neural network

Received: 17 August 2015 Published: 26 December 2016

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TP273

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	Zhao Ximei
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Cite this article:

Zhao Ximei,Zhao Jiuwei. Intelligent Complementary Sliding Mode Control for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2016, 31(23): 9-14.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I23/9

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