Model Predictive Displacement Control Tuning for Tap-Water-Driven Artificial Muscle by Inverse Optimization with Adaptive Model Matching and its Contribution Analyses

The tap-water-driven McKibben artificial muscle has many advantages and is expected to be applied in mechanical systems that require a high degree of cleanliness. However, the muscle has strong asymmetric hysteresis characteristics that depend on the load, and these problems prevent its widespread use. In this study, a novel control method, model predictive control with a servomechanism based on inverse optimization with adaptive model matching, was developed. This control method was applied to the muscle by using a high-precision mathematical model employing an asymmetric Bouc-Wen model. The experimental results show that the proposed approach achieved a high tracking performance for a given reference frequency, with a mean absolute error of 0.13 mm in the steady-state response and with easier controller tuning. Furthermore, the contributions of the controller elements of the proposed method were evaluated. The results show that the contribution of the adaptive system was higher than that of the servo system. Furthermore, the effectiveness of adaptive model matching was verified.