Virtual Reference Feedback Tuning-based Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Robustness Evaluation to Load

Satoshi Tsuruhara, Kazuhisa Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An artificial muscle is well known to have strong asymmetric hysteresis characteristics, which depend on the load applied to the muscle. It is therefore difficult to achieve a high control performance and robustness to various loads. In a previous study, model-free adaptive control (MFAC), which is a data-driven control method, was applied to muscles, and a high tracking control performance was achieved. However, MFAC requires numerous design parameters that are extremely time-consuming to tune. To solve these problems, this study considers the tuning of the design parameters of the MFAC by introducing virtual reference feedback tuning, which is a data-driven control method. In addition, control experiments with five loads were conducted to verify the robustness of the load. The experimental results show that the proposed method achieves a high tracking control performance without an overshoot and is highly robust to loads while reducing the time-consuming routine for parameter tuning and modelling.

Original languageEnglish
Title of host publication2022 European Control Conference, ECC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages221-226
Number of pages6
ISBN (Electronic)9783907144077
DOIs
Publication statusPublished - 2022
Event2022 European Control Conference, ECC 2022 - London, United Kingdom
Duration: 2022 Jul 122022 Jul 15

Publication series

Name2022 European Control Conference, ECC 2022

Conference

Conference2022 European Control Conference, ECC 2022
Country/TerritoryUnited Kingdom
CityLondon
Period22/7/1222/7/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Systems Engineering
  • Control and Optimization
  • Modelling and Simulation

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