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

Tanishka Goyal, Shahid Hussain, Elisa Martinez-Marroquin, Nicholas Brown, Prashant Jamwal

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)796-806
Number of pages11
JournalIEEE Transactions on Medical Robotics and Bionics
Issue number3
Publication statusPublished - 1 Aug 2022


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