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. Note to Practitioners—The synchronization problem of dual drive gantry stage is a research hotspot in the industrial field and its control accuracy and robustness are important indexes that affect the system performance. In this paper, the coupled dynamics model of the gantry system is analyzed and established. In addition, an adaptive robust synchronous control strategy based on RBF neural network is presented to deal with various nonlinearities, mechanical strong coupling constraints and external unknown disturbances in the system, which improves the synchronization accuracy and anti-interference ability of the system. In practical industrial application, the designed controller can be applied to a class of dual-drive gantry systems to ensure the quality of product processing. At the same time, under the strong disturbance of complex working conditions, machine damage or more serious safety accidents caused by asynchronous movement can be avoided and the system reliability can be effectively improved, which is of great significance to industrial production.