Data-Driven Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Parameter Design Using Virtual Reference Feedback Tuning

Satoshi Tsuruhara, Kazuhisa Ito

Research output: Contribution to journalArticlepeer-review

Abstract

A McKibben artificial muscle has strong asymmetric hysteresis characteristics, which depend on the load applied to the muscle. Thus, designing a controller for high-performance displacement is difficult. In a previous study, model predictive control with a servomechanism combining an inverse optimization algorithm with adaptive model matching, and a data-driven model-free adaptive control (MFAC) were introduced. As a result, a high tracking control performance was achieved in both control methods. However, model-based and data-driven approaches require a highly accurate mathematical model and a large number of design parameters, making them time-consuming, respectively. To solve these problems, in the present study, a controller design that requires no precise mathematical model and less design parameter tuning with trial and error was developed by combining conventional MFAC and virtual reference feedback tuning, which is a data-driven control method. Experimental results indicated that important design parameters, such as the initial pseudo-gradient vector and weighting factor, can be readily obtained. Compared with conventional MFAC, higher tracking control performance without overshoot was achieved in transient response, while the same level of control performance was maintained in steady-state response.

Original languageEnglish
Pages (from-to)664-676
Number of pages13
JournalJournal of Robotics and Mechatronics
Volume34
Issue number3
DOIs
Publication statusPublished - 2022 Jun

Keywords

  • data-driven control
  • McKibben artificial muscle
  • model-free adaptive control
  • virtual reference feedback tuning
  • water-hydraulic sys-tems

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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