Abstract
BackgroundCurrently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge.MethodsThe model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced.ResultsValidation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver.ConclusionsThis work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton.
Highlights
Control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions
Performing activities of daily living (ADL) is a challenge for people affected by muscle weakness or neurologic disorders, like paraplegia
Model-based Control Because all controllers do depend on hardware to some extent, modelling procedures and assumptions behind the control design are of great importance for other researchers working on different hardware
Summary
Control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Crutches and wheelchairs provide help to perform ADL These devices do not encourage the normal use of muscles and they lead to disuse and further deterioration of lower-limb muscle function for ADL. The variety in control strategies in assistive robotics is further backed up by the review work of Yan et al [2]. They classify six groups of assistive controllers of which predefined trajectory and model-based control strategies are the most common. Predefined trajectory controllers are often used for gait trainers and exoskeletons for complete paraplegic persons These predefined trajectories are enforced with position or impedance control [3, 4]. Enforcing a trajectory with an impedance controller is undesirable in an exoskeleton supporting voluntary capabilities
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