Partial Memory Iterative Learning Control with Velocity Constraints for Rehabilitative Training Walker under Human-robot Uncertainty

To improve the tracking accuracy and safety of the rehabilitative training walker, an adaptive iterative learning control method with velocity constraints and partial memory information is proposed to reduce the influence of human-robot uncertainty and velocity mutation on the tracking performance of the human-robot system. Taking the human-robot uncertainty into consideration, a dynamic model of the rehabilitative training walker is established. A model prediction based velocity constraints method is proposed, which restricts the actual movement velocity of the robot by limiting the movement velocity of each wheel. Furthermore, a dynamic tracking error system is established by using the constrained movement velocity, a design method of adaptive iterative learning controller with partial memory information is proposed, and the stability of the tracking error system is verified. Comparative simulation analysis and experimental research are carried out, and the results show that the proposed control method can restrain the human-robot uncertainty, and enable the recovered person to walk at a safe velocity.