Impedance Control of a Wrist Rehabilitation Robot Based on Autodidact Stiffness Learning
Goyal T. Hussain S. Martinez-Marroquin E. Brown N.A.T. Jamwal P.K.
1 August 2022Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Medical Robotics and Bionics
2022#4Issue 3796 - 806 pp.
Dynamic control of an intrinsically compliant robot is paramount to ensuring safe and synergistic assistance to the patient. This paper presents an impedance controller for the rehabilitation of stroke patients with compromised wrist motor functions. The control design employs a Koopman operator-based autodidactic system identification model to predict the anatomical stiffness of the wrist joint during its various degrees of rotational motion. The proposed impedance controller, perceiving the level of the subjects participation from their joint stiffness, can modify the applied force. The end-effector robot has a parallel structure that uses four biomimetic muscle actuators as parallel links between the end-effector and the base platform. The controller performance is corroborated by testing the end-effector robot with three healthy subjects.
Anatomical stiffness prediction , biomimetic muscle actuators (BMA) , impedance control , Koopman operator , non-linear control , wrist rehabilitation robot
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University of Canberra, The School of Information Technology and Systems, ACT, Bruce, 2617, Australia
University of Canberra, The Faculty of Health, ACT, Canberra, 2617, Australia
Nazarbayev University, The Department of Electrical and Computer Engineering, Nur-Sultan, 010000, Kazakhstan
University of Canberra
University of Canberra
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026