Design of a dynamics-based hydraulic controller for lifting manipulator wrist and its stability analysis

Under the requirements of Industry 4.0, the performance requirements for improving the hydraulic controller of the mechanical hand wrist are getting higher and higher. To address issues such as response delay and insufficient accuracy in traditional methods, this study proposes a hydraulic control based on a dynamic model. The core innovation lies in embedding the dynamic model into the control loop and integrating multiple intelligent algorithms for closed-loop optimization. Experimental verification shows that the maximum trajectory tracking error of the SHD controller is only 0.28 mm, the fault detection accuracy is as high as 98.3%, and the energy conversion efficiency is 98.1%. It is significantly superior to existing advanced controllers in terms of accuracy, stability, and response speed, such as the controller combining quantum-inspired neural networks with robust control, the controller combining meta-learning with fuzzy wavelet control, and the controller combining federated learning with edge control. The above research results provide an efficient solution for the precise and intelligent control of hydraulic systems in complex lifting operations.