Identification and Control of Rehabilitation Robots with Unknown Dynamics: A New Probabilistic Algorithm Based on a Finite-Time Estimator

Naif D. Alotaibi, Hadi Jahanshahi (), Qijia Yao, Jun Mou and Stelios Bekiros
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Naif D. Alotaibi: Communication Systems and Networks Research Group, Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Hadi Jahanshahi: Institute of Electrical and Electronics Engineers, Toronto, ON M5V3T9, Canada
Qijia Yao: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Jun Mou: School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China

Mathematics, 2023, vol. 11, issue 17, 1-17

Abstract: The control of rehabilitation robots presents a formidable challenge owing to the myriad of uncharted disturbances encountered in real-world applications. Despite the existence of several techniques proposed for controlling and identifying such systems, many cutting-edge approaches have yet to be implemented in the context of rehabilitation robots. This highlights the necessity for further investigation and exploration in this field. In light of this motivation, we introduce a pioneering algorithm that employs a finite estimator and Gaussian process to identify and forecast the uncharted dynamics of a 2-DoF knee rehabilitation robot. The proposed algorithm harnesses the probabilistic nature of Gaussian processes, while also guaranteeing finite-time convergence through the utilization of the Lyapunov theorem. This dual advantage allows for the effective exploitation of the Gaussian process’s probabilistic capabilities while ensuring reliable and timely convergence of the algorithm. The algorithm is delineated and the finite time convergence is proven. Subsequently, its performance is investigated through numerical simulations for estimating complex unknown and time-varying dynamics. The results obtained from the proposed algorithm are then employed for controlling the rehabilitation robot, highlighting its remarkable capability to provide precise estimates while effectively handling uncertainty.

Keywords: knee rehabilitation robot; identification; dynamic estimation; Gaussian process; finite-time estimator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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