Disturbance Observer-Based Adaptive Neural Control of the Permanent Magnet Linear Motor System With Unknown Backlash-Like Hysteresis

In the large-area multidevice linkage-based laser processing, using the traditional control method cannot guarantee the processing accuracy of a permanent magnet linear motor (PMLM) system for a high-speed subject, due to unknown model uncertainties, backlash-like hysteresis, states constraints, and external disturbance. To address this shortcoming, this article develops a disturbance observe-based adaptive neural control for the PMLM system. First, aiming at the problem of unknown and uncertain system parameters, this study uses a radial basis neural network to estimate unknown functions of the system. Then, an adaptive variable is introduced to compensate for the effect of unknown backlash-like hysteresis, and the barrier Lyapunov function is used to restrict the motor from operating in a specified area. In addition, a nonlinear disturbance observer is constructed to adapt to the actual processing to reduce the influence of the external disturbance and the system load change. The proposed control scheme is verified experimentally, and the results indicate that when the proposed method is used, the PMLM system is consistently bounded by using the Lyapunov theorem. Finally, the simulation and experimental results verify the effectiveness of the proposed control strategy. The proposed method could be applied to laser-processing equipment.