An efficient MPC controller for the rehabilitation of an actuated knee joint orthosis

Review of Human-exoskeleton Control Strategy for Lower Limb Rehabilitation Exoskeleton

“…The inputs in position-based control strategies are generally joint angles and angular velocities, and the output usually is the desired control torque of the actuator. A significant portion of the current approaches in position-based control strategies use sliding mode control and its improved control strategies , in order to suppress disturbances and weaken jitter, we can combine sliding mode control with machine learning methods and add certain constraints [57] or use methods such as model predictive control (MPC) [58][59][60][61] to improve the stability. Cao et al [53] designed a dynamic boundary layer-based fast terminal sliding mode control method, as shown in Figure 10, which first converts a pre-given reference trajectory into a joint angle, then uses a dynamic boundary layer approach to suppress the jitter of the system, and finally uses a power-of-two approximation law to compensate the input signal, reducing the modeling error and the environmental interference to the exoskeleton robot and improving the The robustness of the system.…”