Impedance Control of a Wrist Rehabilitation Robot Based on Autodidact Stiffness Learning

• Published in: IEEE transactions on medical robotics and bionics Vol. 4; no. 3; pp. 796 - 806
• Main Authors: Goyal, Tanishka, Hussain, Shahid, Martinez-Marroquin, Elisa, Brown, Nicholas A. T., Jamwal, Prashant K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Anatomical stiffness prediction; biomimetic muscle actuators (BMA); Biomimetics; Controllers; Dynamic control; End effectors; Human motion; Impedance; impedance control; Joints (anatomy); Koopman operator; Medical treatment; Muscles; non-linear control; Parallel robots; Rehabilitation robots; Robots; Stiffness; System identification; Wrist; wrist rehabilitation robot
• ISSN: 2576-3202; 2576-3202
• DOI: 10.1109/TMRB.2022.3194528

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.