Model-free adaptive variable impedance control of gait rehabilitation exoskeleton

Abstract

This paper addresses the challenges of control and human–robot interaction for lower-limb gait rehabilitation exoskeletons. To enhance the robustness of the control against unknown but bounded uncertainties, we propose a model-free adaptive sliding mode control strategy enhanced by a variable impedance approach. The adaptation law prevents the overestimation of control gain in the presence of uncertainty and ensures the sliding condition to mitigate the effects of unknown uncertainties. The variable impedance approach also allows the impedance of the entire system to adapt dynamically over the gait cycle and maintain the accuracy of the robot in tracking desired joint trajectories. We provide a detailed stability proof using Lyapunov theory and demonstrate the finite-time convergence of the defined sliding surface. The proposed strategy does not require knowledge of model parameters, resulting in reduced computational complexity. A lower extremity rehabilitation exoskeleton model was utilized as an illustrative example. To demonstrate the effectiveness of the proposed approach, we conducted several simulations using a lower-limb rehabilitation exoskeleton model. Comparative evaluations were performed against conventional control methods such as the conventional sliding mode and computed torque controllers. The results indicate the effective performance of the proposed controller in the presence of impedance, in reducing the detrimental effects of interaction forces and model uncertainty, as well as accurately tracking the desired gait trajectories.