An Embedded System for Data-Based Self-Sensing in Shape Memory Alloy Wire Actuators

Abstract

Abstract Estimation of displacement of a Shape Memory Alloy (SMA) actuator from electrical properties, is one of the attractive attributes of SMA. To utilize this feature, it is necessary to model the correlation between electrical properties and strain of the wire. Identification of the relationship between resistance and displacement is not always straightforward in SMA actuators, as the complexity of the resistance-displacement characteristics may vary depending upon various factors, e.g., biasing mechanism, additional external loads, actuation frequency, as well as the electrical activation strategy for heating SMA wires. In this paper, we present an experimental setup that incorporates a spring-loaded SMA wire mechanism. Resistance across the SMA wire has been measured for different actuation frequencies. In general resistance behavior has been observed and analyzed, which motivates the use of data-based approaches for estimation of the displacement. Recurrent neural networks represent a possible solution for identifying the complex correlation between electrical input and displacement for different configurations of SMA-actuated systems. The displacement estimated by the trained model is then compared against the displacement measured by a laser displacement sensor, for validation purpose.