Adaptive Event-Triggered Motion Tracking Control Strategy for a Lower Limb Rehabilitation Exoskeleton

Zhinan Peng, Hong Cheng, Rui Huang, Jiangping Hu, Rui Luo, Kaibo Shi, Bijoy Kumar Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent years, lower limb exoskeleton has attracted extensive attention in academic and engineering research. In the rehabilitation motion training scenario, it is of concern that the exoskeleton should have the ability to control its own leg movements in order to assist a patient with natural and anthropomorphic gaits. A patient with paralysis in leg, is incapable of controlling and coordinating with the exoskeleton well enough to produce a desirable gait. Thus, a critical problem in this scenario is that the exoskeleton needs to track a desired gait in order for the patient to adopt and produce different walking patterns. To this end, this paper proposes an adaptive tracking controller for the exoskeleton. Although many adaptive control methods exist, a traditional control design utilizes time-triggered method, namely, the system data is periodically sampled and controller parameters are updated at specific time instances. To implement the update mechanism, in this paper, an aperiodically adaptive controller based on policy iteration is developed by integrating event triggering mechanism and reinforcement learning. Further, in order to achieve online learning and adaptation, an actor-critic neural network is employed, and a novel event triggered tuning law is designed to learn the controller signals. We conduct simulations and perform experiments with a lower limb rehabilitation exoskeleton robot. Experimental results demonstrate that the proposed event triggered control strategy can reduce control updates when compared with many traditional time triggered control methods, for a guaranteed control performance.

Original languageEnglish
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1795-1801
Number of pages7
ISBN (Electronic)9781665436595
DOIs
StatePublished - 2021
Event60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States
Duration: Dec 13 2021Dec 17 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2021-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference60th IEEE Conference on Decision and Control, CDC 2021
Country/TerritoryUnited States
CityAustin
Period12/13/2112/17/21

Keywords

  • Event-Triggered Mechanism
  • Lower Limb Rehabilitation Exoskeleton
  • Motion Tracking Control
  • Neural Network
  • Reinforcement Learning

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