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Actuators 2016, 5(4), 25; doi:10.3390/act5040025

Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
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Author to whom correspondence should be addressed.
Academic Editor: Jose Luis Sanchez-Rojas
Received: 2 September 2016 / Revised: 12 October 2016 / Accepted: 2 November 2016 / Published: 8 November 2016
(This article belongs to the Special Issue MEMS-based Actuators)
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Abstract

As a new actuating material, magnetic controlled shape memory alloys (MSMAs) have excellent characteristics such as a large output strain, fast response, and high energy density. These excellent characteristics are very attractive for precision positioning systems. However, the availability of MSMAs in practical precision positioning is poor, caused by weak repeatability under a certain stimulus. This problem results from the error of a large magnetic hysteresis in an external magnetic field. A suitable hysteresis modelling method can reduce the error and improve the accuracy of the MSMA actuator. After analyzing the original hysteresis modelling methods, three kinds of hysteresis modelling methods are proposed: least squares method, back propagation (BP) artificial neural network, and BP artificial neural network based on genetic algorithms. Comparing the accuracy and convergence rate of three kinds of hysteresis modelling methods, the results show that the convergence rate of least squares method is the fastest, and the convergence accuracy of BP artificial neural networks based on genetic algorithms is the highest. View Full-Text
Keywords: magnetic controlled shape memory alloy; actuator; hysteresis modelling method; hysteresis curve; optimization magnetic controlled shape memory alloy; actuator; hysteresis modelling method; hysteresis curve; optimization
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Tu, F.; Hu, S.; Zhuang, Y.; Lv, J.; Wang, Y.; Sun, Z. Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator. Actuators 2016, 5, 25.

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