Low-speed performance research for permanent magnet synchronous linear motor based on nonparametric model learning adaptive control

Based on influence of friction, permanent magnet synchronous linear motor (PMSLM) with contact surface frequently appears rough running and control precision deterioration during low-speed operation. The design of nonparametric model learning adaptive control(NMLAC) algorithm in linear motor system...

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Bibliographic Details
Main Authors: Cao Rongmin, Zhou Huixing, Hou Zhongsheng, Wu Yingnian
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Artificial neural networks
Force
Friction
Mathematical model
Permanent magnet motors
Prediction algorithms
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Summary:Based on influence of friction, permanent magnet synchronous linear motor (PMSLM) with contact surface frequently appears rough running and control precision deterioration during low-speed operation. The design of nonparametric model learning adaptive control(NMLAC) algorithm in linear motor system is studied, estimation of pseudo-partial-derivatives is discussed, controller is based directly on pseudo partial-derivatives derived on-line from the input and output information of PMSLM using recursive least squares type of identification algorithms. The simulation control results show that the algorithms exhibit such advantages as good robustness, PMSLM low-speed response, against exogenous disturbance and noise for time-varying systems with vaguely known dynamics, the proposed method can realize good online friction estimation and compensation, hence the control performance of PMSLM is improved and it outperforms traditional PID controller and neural networks(NN) control method.
DOI:10.1109/ICEMS.2011.6073732