Min Zhang

In the research on consensus tracking control for Lower Limb Rehabilitation Robotic Systems (LLRRS), it is crucial to ensure that all state variables of the LLRRS, including initial state, angle, and angular velocity, converge towards a consensus. This paper addresses the motion tracking control issue of LLRRS in scenarios with initial state deviations. Firstly, a dynamic mathematical model of the LLRRS is established, and the target motion trajectory is determined. To tackle the challenges posed by initial state deviation, a closed-loop PD-type accelerated iterative learning controller with initial state learning is designed, utilizing only the output measurements of the system and a variable learning gain factor. The applicability of this controller for achieving consensus tracking control of the LLRRS state variables is verified through mathematical analysis and simulation. Finally, the feasibility and effectiveness of the proposed algorithm are corroborated through experimental prototype testing. The experimental results demonstrate that the maximum tracking error for the hip joint angle of the LLRRS is 7.14°, and the maximum tracking error for the knee joint angle is 5.74°.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR’s right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.