Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton

Mohammad Soleimani Amiri, Rizauddin Ramli () and Ahmad Barari
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Mohammad Soleimani Amiri: Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Rizauddin Ramli: Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
Ahmad Barari: Advanced Digital Design, Manufacturing, and Metrology Laboratories (AD2MLabs), Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada

Mathematics, 2023, vol. 11, issue 7, 1-14

Abstract: A wearable lower-limb rehabilitation exoskeleton functions to fulfill the recovery process of limb functionality and assist physiotherapists. This paper presents an optimized adaptive control system for a wearable lower-limb rehabilitation exoskeleton. The tuning of the controller gains is defined as an optimization problem for a closed-loop control system of the wearable lower-limb rehabilitation robot by genetic algorithm and particle swarm optimization. We presented a novel initialized model reference adaptive controller (IMRAC) for real-time joint trajectory tracking, in which controller gains are adjusted by the gradient-based method. An experimental test of a 4-degree of freedom lower-limb rehabilitation exoskeleton was carried out to observe the closed-loop performance of IMRAC for bipedal human walking. The statistical comparison between IMRAC and MRAC shows an efficient performance and robustness of our proposed method for the joint trajectory tracking of the lower-limb rehabilitation exoskeleton in real time.

Keywords: model reference adaptive controller; exoskeleton; genetic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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