Markovian Robust Filtering and Control Applied to Rehabilitation Robotics

• Published in: IEEE/ASME transactions on mechatronics Vol. 26; no. 1; pp. 491 - 502
• Main Authors: Escalante, Felix M., Jutinico, Andres L., Jaimes, Jonathan C., Terra, Marco H., Siqueira, Adriano A. G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuation; Dynamics; Force; Human engineering; Human performance; IEEE transactions; Impedance; Kalman filters; Linear quadratic regulator; Linear systems; Markovian jump linear systems (MJLS); Mechatronics; Parameters; Rehabilitation; Rehabilitation robots; Robot control; Robot kinematics; robotic rehabilitation; Robotics; Robots; Robust control; robust estimation
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2020.3034245

In this article, we deal with rehabilitation robot robust control in order to obtain best performance in the human-robot interaction. This class of system is subject to abrupt changes due to exogenous inputs and parameter variations caused, mainly, by the human dynamic behavior. In this context, Markovian jump linear systems offer suitable tools to model and control of this class of interactions. We propose force and impedance controllers based on robust recursive Markovian versions of the standard Kalman filter and the linear quadratic regulator. Different levels of the individual's actuation during the interaction with the robot define the Markov states. The measurement of electromyographic signals is used as jump parameter among Markov states. Also, a serious game is used to generate visual feedback, to promote active user participation, and to guide his movement routine. We compare our proposal with two other Markovian-based approaches and show the effectiveness our method through experiments with an ankle rehabilitation platform.