Decentralized Adaptive Impedance Control of Reconfigurable Robot Manipulators for Human Robot Collaboration

For the problem of dynamic contact force tracking control under human robot collaboration (HRC), we propose a dual closed-loop decentralized adaptive control framework. The dynamic model of reconfigurable robot manipulator (RRM) subsystem is established based on joint torque feedback (JTF) technology. On the basis of fully analyzing the model uncertainty, the method based on decomposition is used to dynamically compensate the model uncertainty. Using Lyapunov theory, the uniform and ultimate boundedness (UUB) of dynamic contact force tracking error and RRM position tracking error in HRC process are confirmed. A neural network (NN) observer is designed to dynamically compensate the uncertainty of controller. Finally, the effectiveness of this method is verified by experiments.