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IJAT Vol.19 No.3 pp. 291-303
doi: 10.20965/ijat.2025.p0291
(2025)

Research Paper:

Hierarchical-Type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching

Satoshi Tsuruhara*,† ORCID Icon and Kazuhisa Ito** ORCID Icon

*Graduate School of Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Saitama 337-8570, Japan

Corresponding author

**Department of Machinery and Control Systems, Shibaura Institute of Technology
Minuma, Japan

Received:
August 27, 2024
Accepted:
January 14, 2025
Published:
May 5, 2025
Keywords:
adaptive model matching, data-driven control, model predictive control, water hydraulic artificial muscle, displacement control
Abstract

High-precision displacement control for water-hydraulic artificial muscles is challenging because of their strong hysteresis characteristics, which are difficult to be modeled precisely. Recently, data-driven control methods have attracted considerable attention because they do not explicitly use mathematical models, making the design much easier. In our previous work, we proposed a fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle, and showed its effectiveness because it can also consider input constraints. However, the problem in which control performance strongly depends on prior input-output data remains unsolved. Adaptive FRIT (A-FRIT) based on directional forgetting has also been proposed; however, achieving the desired transient performance is difficult because it cannot consider the input constraints, and there are no design parameters that directly determine the control performance. This paper proposes a novel data-driven adaptive model matching-based controller that combines MPC with the A-FRIT. The experimental results show that the proposed method can significantly improve the control performance and achieve high robustness against inappropriate initial experimental data while considering the input constraints in the design phase.

Cite this article as:
S. Tsuruhara and K. Ito, “Hierarchical-Type Model Predictive Control and Experimental Evaluation for a Water-Hydraulic Artificial Muscle with Direct Data-Driven Adaptive Model Matching,” Int. J. Automation Technol., Vol.19 No.3, pp. 291-303, 2025.
Data files:
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Last updated on May. 08, 2025