Optimal control of manipulator with joint clearance compensation via generalized policy learning

Abstract

To enhance the manipulator's motion accuracy, a Hertz collision force model for the hinge positions and a dynamic model for the manipulator is established, a novel adaptive clearance compensation algorithm is proposed to counteract the nonlinear effects induced by joint clearance. This article proposes a novel adaptive optimal clearance compensation tracking control method for dynamic manipulator systems with joint clearance. The method simultaneously computes feedforward and feedback control actions through an enhanced system approach based on Adaptive Dynamic Programming (ADP) and a performance index function. To implement the optimal control strategy, a generalized policy learning algorithm is developed, which reduces the dependency on known system dynamics. Additionally, the algorithm enables continuous, synchronous updates of adaptive evaluation and control actions, eliminating the need for iterative steps. Unlike traditional approaches, this method discards the use of behavioral neural networks (ANNs), thereby reducing computational complexity. Simulation results demonstrate the effectiveness of the proposed learning algorithm and control method for manipulator clearance compensation. The effectiveness of the clearance compensation method was further validated through experiments conducted on the robotic arm test platform. By implementing clearance compensation-based optimization control, the Integral Absolute Error (IAE) of manipulator link1 and link2 displacement was reduced by 54.2% and 40.8%, respectively, compared to the uncompensated clearance state.