Abstract：A nonlinear disturbance observer (NDO) control method based on wavelet neural network (WNN) is used to improve the performance of the permanent magnet linear synchronous motor (PMLSM), which is easily influenced by nonlinear uncertainties such as external load disturbance, parameter change and friction, etc. Firstly, linearize the nonlinear model. Then, the linear system theory is used to design the feedback linearization controller (FLC) for the main position tracking to make the PMLSM control system stable. The uncertainties of the system is estimated and compensated by NDO, and the system tracking error is reduced. However, in the actual operation, the observer gain is difficult to select, and it is very easy to produce large observation errors. In order to enhance the robustness of the system, compensating observation errors online through WNN to improve the compensation ability of NDO. The experimental results show that the proposed method is effective, the system has strong robustness and good tracking accuracy, which can effectively compensate for the uncertainties existing in the system.
赵希梅, 原浩, 朱文彬. 基于小波神经网络和非线性扰动观测器的直线伺服系统控制[J]. 电工技术学报, 2019, 34(19): 3989-3996.
Zhao Ximei, Yuan Hao, Zhu Wenbin. Control of Linear Servo System Based on Wavelet Neural Network and Nonlinear Disturbance Observer. Transactions of China Electrotechnical Society, 2019, 34(19): 3989-3996.
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