Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification

Abstract

This study proposed a control method, a data-driven predictive control (DDPC), for the hand exoskeleton used for active, passive, and resistive rehabilitation. DDPC is a model-free approach based on past system data. One of the strengths of DDPC is that constraints of states can be added to the controller while performing the controller design. These features of the control algorithm eliminate an essential problem for rehabilitation robots in terms of easy customization and safe repetitive rehabilitation tasks that can be planned within certain constraints. Experiments were carried out with a designed hand rehabilitation system under repetitive and various therapy tasks. Real-time experiment results demonstrate the feasibility and efficiency of the proposed control approach to rehabilitation systems.

Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton

“…Furthermore, assist-asneeded exoskeleton of the upper limb has been proposed specially for shoulder and wrist, with very few studies on the hand and finger movements [45], among assist-as-needed exoskeletons of the hand [44,46]. In this regard, the model predictive control (MPC) is a popular technique with the ability to handle constraints, optimizing the control output while considering the future states of the exoskeleton [47].…”