Optimal Admittance Control of Human–Robot Interaction for Robotic Rehabilitation Under Uncertainties

This paper presents a human–robot interaction (HRI) scheme by using an adaptive admittance control, which helps stroke patients perform rehabilitation training tasks and optimizes their performance. Considering the impact of human factors, the control structure is designed to have two control loops. In the inner loop design, a model‐free adaptive control (MFAC) method is proposed to handle the unmodeled dynamics and unknown disturbances for the desired trajectory tracking, and the convergence and boundedness of this method are strictly proved by using the compression mapping principle. Then, a task‐specific outer loop is developed to find the optimal parameters of the admittance model and transformed into an LQR problem, and a learning algorithm is utilized to solve the given problem without requiring knowledge of the human arm model. Considering the safety of HRI, the constraint of the end‐effector orientation is designed. Simulation studies indicate that the proposed strategy effectively enables stroke patients to execute active training tasks on the robotic exoskeleton.