High-Performance Robust Synchronous Control of Dual-Drive Gantry System via Composite Adaptive B-Spline Wavelet Neural Network

Complex uncertain dynamics and external disturbances have always been critical factors restricting the performance improvement of dual-drive gantry system (DDGS), especially the synchronization and robustness. To enhance the performance of DDGS in uncertain industrial environments, in this paper, a high-performance robust control method is proposed based on a novel composite adaptive B-spline wavelet neural network (CABWNN) estimator. Specifically, a composite adaptive robust synchronization control method with desired compensation is proposed based on a rotational dynamic coupling model of the system. This method combines the benefits of both direct and indirect adaptive robust control, achieving superior control performance while simultaneously mitigating the impact of measurement noise in the system. Additionally, a novel CABWNN estimator with a specially designed composite adaptive weight updating law is introduced to approximate the system uncertainties, which enhances the approximation accuracy. Under the proposed control method, the boundedness of all signals in the closed-loop has been proved in theory. Furthermore, comparative experiments on a dual-drive gantry system validate the superiority of the proposed control strategy.