基于递归径向基神经网络的永磁直线同步电机智能二阶滑模控制

doi:10.19595/j.cnki.1000-6753.tces.191238

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Abstract The permanent magnet linear synchronous motor (PMLSM) is susceptible to uncertainty factors, such as system parameter variation, external disturbance and friction. Thus, an intelligent second-order sliding mode control (I2OSMC) method combining second-order sliding mode control (2OSMC) and recurrent radial basis function neural network (RRBFNN) is used to improve system control performance. The design of 2OSMC weakens the chattering problem in the traditional sliding mode control and improves the position tracking accuracy of the system. However, because it is difficult to estimate the boundary of the uncertainty factors in the system, the optimal performance of 2OSMC cannot be achieved. Therefore, the RRBFNN is introduced to improve the robustness of the system, which has faster learning ability and can train the network parameters online. The experimental results show that the proposed control method is feasible and can effectively suppress the influence of uncertainty factors on the control system, so that the system has higher position tracking accuracy and stronger robust performance.

Key words： Permanent magnet linear synchronous motor uncertainty factors second-order sliding mode control recurrent radial basis function neural network

Received: 24 September 2019

PACS:	TM351
	TP273

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	Wang Tianhe
	Zhao Ximei
	Jin Hongyan

Cite this article:

Wang Tianhe,Zhao Ximei,Jin Hongyan. Intelligent Second-Order Sliding Mode Control Based on Recurrent Radial Basis Function Neural Network for Permanent Magnet Linear Synchronous Motor[J]. Transactions of China Electrotechnical Society, 2021, 36(6): 1229-1237.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191238 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I6/1229

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