RBF Neural Network-Based Adaptive Robust Synchronization Control of Dual Drive Gantry Stage With Rotational Coupling Dynamics

As a typical mechatronics system, dual drive gantry stage has been widely used in high-end intelligent equipment. In this paper, an adaptive robust synchronization control scheme based on RBF neural network is presented to improve the synchronization accuracy and robust performance of dual drive gantry system. In order to overcome the limitation of system performance caused by ignoring high-frequency rotation mode in traditional modeling, a more reasonable rotational dynamic coupling model of gantry table was established. In addition, the adaptive robust control method with expected compensation is adopted to avoid the interference of measurement noise in the system and realize accurate compensation of the model. The advantages of RBF neural network infinite approximation are used to deal with the effects of model compensation residual, unmodeled dynamics and uncertain disturbances. The stability of the closed-loop system is proved by the Lyapunov theorem. Finally, different control strategies are used to conduct comparative experiments and the experimental results verify the superiority and effectiveness of the proposed control strategy.