Abstract：The position tracking accuracy of permanent magnet linear synchronous motor (PMLSM) servo system is susceptible to parameter variation, load disturbance, friction and other uncertain factors. Therefore, a backstepping terminal sliding mode control method based on radial basis function (RBF) neural network is proposed. Firstly, the PMLSM dynamic mathematical model with uncertainty is established. Then, the state of the system converges to the equilibrium point in a finite time and the response speed of the system is improved by using the backstepping terminal sliding mode control. In order to further weaken chattering phenomenon, the saturation function is designed combined the hyperbolic tangent function with boundary layer thickness to replace the signum function. Moreover, RBF neural network is used to approximate the uncertainties in the system, and then fast-tracking performance and strong immunity ability are obtained. Finally, the experimental results show that the proposed control method not only improves the tracking and robustness of the system, but also significantly weakens the chattering problem.
付东学, 赵希梅. 基于径向基函数神经网络的永磁直线同步电机反推终端滑模控制[J]. 电工技术学报, 2020, 35(12): 2545-2553.
Fu Dongxue, Zhao Ximei. Backstepping Terminal Sliding Mode Control Based on Radial Basis Function Neural Network for Permanent Magnet Linear Synchronous Motor. Transactions of China Electrotechnical Society, 2020, 35(12): 2545-2553.
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2020, Vol. 35 
