LS-SVM Based Modeling and Model Predictive Control for a Water-Hydraulic Artificial Muscle Actuator

Abstract

Artificial muscle actuators (AMAs) driven by the pressure of tap water are flexible and lightweight and are therefore safe for humans and suitable for wearable power-assist devices. This paper proposes a modeling approach for a water-hydraulic AMA based on least squares support vector machines (LS-SVMs). Modeling tests are carried out on this LS-SVM approach with experimentally acquired data and show that the proposed model can capture the hysteretic characteristics of the water-hydraulic AMA. In addition, we constructed a state-space model of the water-hydraulic AMA into which the proposed LS-SVM-based model was incorporated and propose a model predictive control system based on the state-space model. An experimental comparison of the proposed control system and a control system with an inverse model of the water-hydraulic AMA, used in our previous study, demonstrated that the proposed one exceeds the previous one in control performance.

• artificial muscle actuator
• water-hydraulic system
• least squares support vector machine
• hysteresis
• model predictive control