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

TL;DR

This paper introduces a novel adaptive control method for water-hydraulic artificial muscles that improves precision and robustness by combining data-driven and model-based approaches, effectively handling hysteresis and input constraints.

Contribution

It proposes a new data-driven adaptive model matching controller that enhances control performance and robustness, addressing limitations of previous methods in handling initial data and input constraints.

Findings

Significant improvement in control accuracy and robustness.

Effective handling of hysteresis in water-hydraulic muscles.

Successful experimental validation demonstrating advantages over prior methods.

Abstract

High-precision displacement control for water-hydraulic artificial muscles is a challenging issue due to its strong hysteresis characteristics that is hard to be modelled precisely, and many control methods have been proposed. Recently, data-driven control methods have attracted much attention because they do not explicitly use mathematical models, making design much easier. In our previous work, we proposed 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 consider input constraints as well. However, the problem in which control performance strongly depends on prior input-output data remains still unsolved. Adaptive FRIT based on directional forgetting has also been proposed; however, it is difficult to achieve the desired transient performance because it cannot consider input constraints and there are no design parameters that directly determine the control performance, such as MPC. In this study, we propose a novel data-driven adaptive model matching-based controller that combines these methods. Experimental results show that the proposed method could significantly improve the control performance and achieve high robustness against inappropriate initial experimental data , while considering the input constraints in the design phase.