Research On Linear Permanent Magnet Synchronous Motor Drives With Neural Network Sliding Mode Controller

Sliding mode variable structure (SMVS) is a nonlinear control method, which is adaptive to disturbance and parameter variations. Especially, SMVS control is stable and robust against system uncertainties. However, the chattering exists in sliding mode control. Neural network control strategy is widely employed because of its nonlinear imaging, self- learning, error tolerance, parallel processing abilities and so on, but slow response, complicated output control etc. are is shortcomings. The integrated advantages of both schemes can improve system performances.This thesis discusses the contradiction between stability and rapid response in linear permanent magnet synchronous motor drives. By optimum design of control scheme, fast tracking characteristic to the command signal and suppression of parameter variation and perturbation are obtained. Therefore, the control accuracy of linear servo system is increased to a great extent.A dual degree-of-freedom control scheme based on combining nonlinear neural network control and SMVS for a direct drive AC permanent magnet linear servo system is presented in this thesis. The sliding mode input controller ensures the system has the fast tracking characteristic to the command signal. While the neural network output suppress parameter variations and resistance perturbation (including the end effect force produced by of linear motor) greatly, and can effectively minimize the chattering of sliding mode control, which could influence the steady state performance of the system. Some simulation results show that this control scheme cannot only enhance track-command ability and the robustness of the linear servo system, but also has strong robustness to parameter variations and resistance perturbation.The proposed conclusions have guiding significance for engineering design.