Extended-State-Observer-Based Optimized Control of Hydraulic Manipulators

Dynamic model identification for high-dimension hydraulic manipulators remains a significant challenge, with system nonlinearities and dynamic coupling being the primary obstacles to achieving high-accuracy control. To this end, this article introduces the Collaborative-Optimization-Independent-Control (COIC) framework. Within the COIC framework, an extended-state-observer-based joint-independent controller is adopted to handle unmodeled dynamics individually at each joint. Given the coupling among the unmodeled dynamics across different joints, the adjustment of observer gains for all joint-independent controllers is formulated as a collaborative game problem. Reinforcement learning is thereby introduced to solve this game problem and determine the collaborative Nash equilibrium, thereby enabling optimal observer gain configuration and enhancing overall control performance. Theoretical analysis confirms the Lyapunov stability of the joint-independent control system. Furthermore, it is demonstrated that updating the observer gains within the stable region yields (sub)optimal solutions corresponding to the collaborative Nash equilibrium. The effectiveness and advantages of the proposed COIC framework are validated through comparative experiments on a well-established six-degree-of-freedom (6-DOF) hydraulic manipulator platform.