Parameter Estimation and Adaptive Control of Super-coiled Polymer Artificial Muscles

Abstract

Super-coiled polymer (SCP) artificial muscles demonstrate desirable properties such as high-power density, compliance, and low-cost. However, their performance is dependent on the ambient environment. It is necessary but difficult to re-identify key SCP parameters to maintain desired performance. In this work, an adaptive parameter estimation method is proposed to identify unknown SCP parameters. The SCP model identification demonstrates parameter convergence which indicates that SCPs could be controlled effectively and used in environments with variations. We further develop a control law using direct model reference adaptive control (MRAC) on the output position of the SCP actuator. The MRAC performance is compared to a proportional-integral (PI) and proportional-derivative (PD) with feedforward controllers to demonstrate the ability to reduce tracking errors. The MRAC showed superior performance in comparison to the two controllers when the SCP actuator parameters were unknown.