A self-sensing and robust resistance phase transition detection method for the displacement estimation of shape memory alloy wires

Abstract

Shape memory alloy (SMA) wires are widely used as actuators because of their simplicity, high energy ratio, and Joule heat activation. The resistance behavior caused by the phase transition can predict the stress or strain of the SMA wire, which is called self-sensing. At present, there are researches on detecting the start and end points of phase transition based on resistance. However, the anti-interference ability of these researches is poor, and it is difficult to identify the thermal equilibrium points. Therefore, this paper proposes a novel resistance-based phase transition detection method with good robustness and displacement estimation. Firstly, the excitation signal is optimized for the thermal cycle pretreatment of the SMA wire. Secondly, the interferences of the resistance signal are determined, which are noise, small current signals, and step signals. Finally, using filtering and outlier processing, a phase transition region detection algorithm based on the difference of resistance extreme values is proposed. Moreover, a resistance-displacement model is established through linear regression. The experimental results show that the proposed method effectively resists the interference of signals and has good robustness. The mean relative error between the estimated and measured displacements is 4.43%. The resistance-displacement model effectively estimates the displacement of the SMA wire actuator and distinguishes the thermal equilibrium points and the end points of the phase transition. The proposed method can be used potentially for real-time phase transition detection, overheating protection, power consumption reduction, and self-sensing drive of sensorless actuators.