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An Assistive Explicit Model Predictive Control Framework for a Knee Rehabilitation Exoskeleton

Abstract : This article focuses on the control of an actuated knee joint orthosis. The proposed solution is a novel model predictive control framework dedicated to assistive and rehabilitation purposes. This framework includes 1) an exact input-to-state feedback linearization, 2) a model predictive controller (MPC or EMPC), considering input/output constraints, 3) a least-squares dynamic parameters identification, 4) a nonlinear disturbance observer for the estimation of the wearer's torque, 5) a Lyapunov-based stability analysis of the resulting closed-loop system, and 6) a reference trajectory generator. The proposed framework has been validated via real-time experiments performed on three healthy subjects wearing the knee joint orthosis. Various experimental scenarios have been considered, including assistive and resistive rehabilitation tasks in a sitting position and walking with normal/abnormal gait patterns. The obtained results indicate the efficiency of the proposed predictive controllers with respect to a conventional proportional-integral-derivative (PID) controller in terms of tracking performance, required torque, and wearer comfort.
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Contributor : Ahmed Chemori Connect in order to contact the contributor
Submitted on : Monday, December 20, 2021 - 10:35:02 AM
Last modification on : Wednesday, October 26, 2022 - 4:02:54 AM


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Ines Jammeli, Ahmed Chemori, Huiseok Moon, Salwa Elloumi, Samer Mohammed. An Assistive Explicit Model Predictive Control Framework for a Knee Rehabilitation Exoskeleton. IEEE/ASME Transactions on Mechatronics, 2022, pp.3636-3647. ⟨10.1109/TMECH.2021.3126674⟩. ⟨lirmm-03495187⟩



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