Abstract：For the problem that the permanent magnet linear synchronous motor (PMLSM) is vulnerable to the influence of nonlinear factors such as system parameters change and external disturbance and so on, the control performance of the servo system is reduced. A complementary sliding mode control method based on Elman neural network is proposed. The complementary sliding mode control is based on the conventional sliding mode control, adding a generalized error sliding surface, which can not only reduce the system state to the sliding surface time, but also guarantee the system tracking accuracy. However, in practical applications, the switching gain and the value of boundary layer thickness are difficult to select in the complementary sliding mode control. In order to accurately estimate the value of the uncertain factors in the system and to weaken the chattering phenomenon of the sliding mode control, the Elman neural network estimator is used to estimate the value of the uncertain factors, instead of the switching control in the sliding mode control, the influence of uncertain factors on the servo control system is reduced, and the robustness of the system is further improved. The experimental results show that the complementary sliding mode control based on Elman neural network not only improves the position tracking performance of the system, but also increases the robust performance of the system compared with the complementary sliding mode control.
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2018, Vol. 33 
