Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator

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.

A Review on Parametric Dynamic Models of Magnetorheological Dampers and Their Characterization Methods

“…where the z refers to the expression in (8), m takes into account the inertial and MR fluid stiction effect, k 0 is the stiffness of the accumulator, c(ẋ) is the post-yield damping coefficient that addresses the MR fluid shear thinning effect by means of a decreasing function it is assumed to be:…”

“…One of the areas in which their possible applications are more fruitful, regards the damping of the mechanical vibrations. In this field, different materials have been proposed: piezoelectric materials ( [1][2][3][4][5][6]), shape-memory alloys ( [7][8][9]), electro-rheological fluids ( [10]), magnetorheological (MR) fluids, etc. The magnetorheological fluids are composed of a suspension of magnetically responsive particulate matters in a liquid phase.…”

Variable Gap Sealing Technology of a Hydraulic Cylinder Based on Magnetic Shape Memory Alloy

“…Before the magnetic field strength reaches saturation, the higher the magnetic field strength, the greater the elongation of the MSMA, the smaller the gap between the hydraulic cylinder movement pairs, and the less the leakage. However, under the action of the alternating magnetic field, the relationship between the magnetic field strength H and the magnetic induction intensity B in ferromagnetic materials is mostly nonlinear or singlevalued [22,23]. The change in magnetic induction intensity B inside the material lags behind the change in magnetic field strength H, resulting in the hysteresis effect, as shown in Figure 6, increasing the leakage of the hydraulic cylinder during the initial movement.…”

Electromagnetic Actuator System Using Witty Control System

“…Due to their good learning capability, many neural networks (NNs) have been applied in many linear and nonlinear systems, such as optimal hysteresis modelling methods of magnetic controlled shape memory alloys (MSMAs) [5], control of ionic electroactive polymer actuators [6], predicting the driving force for a multicyclic driving experiment of silicone/ethanol soft composite material actuators [7], and design of a 2-DOF ankle exoskeleton with a bidirectional tendon-driving actuator and a polycentric structure [8]. These NNs take a long time to conduct training and learning of a system.…”