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Design and Control Parameter Optimization of Soft Ankle Exoskeleton for People with Dropfoot and Excessive Inversion

Time: Mon 2023-12-04 10.00

Location: D31, Lindstedtsvägen 5, Stockholm

Language: English

Subject area: Engineering Mechanics

Doctoral student: Xiaochen Zhang , Teknisk mekanik, KTH MoveAbility Lab

Opponent: Professor Shaoping Bai, Aalborg University

Supervisor: Professor Elena Gutierrez-Farewik, ; Assistant professor Ruoli Wang, ; Associate professor Susanne Palmcrantz,

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QC 231115

Abstract

Wearable robotics and exoskeletons have been explored for their efficacy in physical rehabilitation and for assistance in daily activities for people with motor disorders. The overall objective of this thesis is to design a powered soft exoskeleton for people with dropfoot and excessive inversion, commonly after a stroke, and to optimize the control parameters for each individual while considering different dimensions. This compilation thesis is based on two papers that focus on the design and verification of the ankle joint exoskeleton prototype, and control parameters optimization using human-in-the-loop optimization, respectively.

In the first paper, we presented the design of the powered soft ankle exoskeleton, mainly consisting of the actuation system, Bowden cables, and textile components, to assist two degrees of freedom (DoF), dorsiflexion and eversion, simultaneously.A proof-of-concept study was performed to verify the functionality of the exoskeleton in two aspects: assisting/controlling two DoFs simultaneously and compensating for the resistance during ankle plantarflexion. Our results suggested that two-DoF assistance can be delivered with the structure, and the proposed force-free controller can counteract the inherent resistance in the system.

In the second paper, a multi-objective-based human-in-the-loop optimization method was proposed, aiming at optimizing gait quality in different aspects simultaneously.In this case study, the multi-objective optimization method, Non-dominated Sorting Genetic Algorithm II, was implemented in the human-in-the-loop optimization. Four generations, comprising ten sets of control parameters in each generation, were tested on one non-disabled subject wearing the exoskeleton described in paper I. The results indicated that this novel method can identify the control laws that optimize both gait quality metrics. In the set of solutions, control laws with different focuses can be selected for different purposes or individual uses.

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