Neural impedance adaption for assistive human-robot interaction

The problem of assistive human-robot interaction (HRI) with unknown impedance parameters is nontrivial and interesting. This problem becomes even more challenging if unknown reference trajectory and uncertain robot dynamics are involved. This study investigates an intelligence impedance adaption control scheme to assist human interaction with an unknown robot system. An algorithm is proposed to facilitate assistive HRI by optimizing the overall human-robot interaction performance. Neural networks (NN) and backpropagation are employed to tackle the optimization problem, based on an online adaption of impedance parameters. The tuned impedance model is integrated into the design of the neuroadaptive controller. The controller is modified by utilizing the barrier Lyapunov function technique to increase the safety, and to improve functionality of the NN during the system operation. The obtained controller can learn the robot dynamics online while coping with both the problems of trajectory-following and impedance model-following. Stability and uniform boundedness of the closed-loop system are verified through Lyapunov direct analysis. The effectiveness of the proposed control design is validated by theoretical analysis and numerical simulation.