Safety control of precision motion system with gantry structure based on fault-tolerant gradient descent B-spline wavelet neural network

The safety control of precision motion equipment in modern industrial fields is a key focus of industrial research, directly affecting the accuracy and lifespan of motion equipment. This paper presents a fault-tolerant gradient descent B-spline wavelet neural network (FTGDBNN) based controller of precision motion equipment for a dual-drive gantry system (DDGS), ensuring the safety and effectiveness of DDGS precision system control. The proposed controller contains the loss-of-effectiveness fault estimator and the gradient descent B-spline wavelet neural network (GDBNN) based compensator that can observe and compensate for loss-of-effectiveness and additive actuator faults in real time. In addition to the actuator additive faults, GDBNN-based compensators can suppress the impact of nonlinear disturbances such as system parameter uncertainties and fault estimator errors on precision equipment. Moreover, The boundedness of the fault estimator and the stability of the entire closed-loop system are theoretically proven. Finally, the safety and effectiveness of the proposed control strategy are validated through a series of fault experiments on the DDGS platform. The experimental results indicate that FTGDBNN has better safety and control performance compared to other control strategies applied to precision systems, especially in high curvature and extreme motion conditions.