Particle filter based self sensing Shape Memory Alloy wire actuator under external cooling

Abstract

Shape Memory Alloy wires are widely used as actuators due to their ability to generate a wide range of force and displacement. In practice, these wires are activated following resistive heating by passing an electrical current through them, whereas deactivation is enacted through environmental cooling or forced cooling. It is observed that for fine wires, the temperature is highly sensitive to ambient conditions, i.e., temperature, the velocity of wind, humidity etc., thus severely affecting the performance of these wire actuators. In this study, a particle filter based state estimation technique has been developed to estimate the outcome of the Shape Memory Alloy wire actuated system, by using the change in electrical resistance data of the wire during actuation. Under stationary ambience, the developed particle filter based estimator performs as good as the EKF based estimators (Mohan and Banerjee (2021)). However, both estimators fail while an unwanted external cooling is introduced through a fan. To improve on it, both filters are modified, incorporating convective heat transfer coefficient as an unknown to be estimated in addition to the system’s state. The performance of both estimators are evaluated for various loading conditions in the presence of forced cooling of different extents and intensities. Under external cooling conditions, the proposed Particle Filter based estimator outperforms the same based on EKF in almost all loading situations.