JRM Vol.34 No.3 pp. 664-676
doi: 10.20965/jrm.2022.p0664


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

Satoshi Tsuruhara* and Kazuhisa Ito**

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

**Department of Machinery and Control Systems, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama 337-8570, Japan

December 21, 2021
April 2, 2022
June 20, 2022
model-free adaptive control, virtual reference feedback tuning, data-driven control, water-hydraulic systems, McKibben artificial muscle

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.

Block diagram of the VRFT-based MFAC

Block diagram of the VRFT-based MFAC

Cite this article as:
S. Tsuruhara and K. Ito, “Data-Driven Model-Free Adaptive Displacement Control for Tap-Water-Driven Artificial Muscle and Parameter Design Using Virtual Reference Feedback Tuning,” J. Robot. Mechatron., Vol.34 No.3, pp. 664-676, 2022.
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Last updated on Jun. 03, 2024