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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Feed-forward control for magnetic shape memory alloy actuators based on the radial basis function neural network

Miaolei Zhou1, Yifan Wang1, Rui Xu1

  • 1Department of Control Science and Engineering, Jilin University, Changchun - PR China.

Journal of Applied Biomaterials & Functional Materials
|May 20, 2017
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Summary

Hysteresis in magnetic shape memory alloy (MSMA) actuators is reduced using a novel radial basis function neural network (RBFNN) model. This approach achieves high-precision control, overcoming application limitations of MSMA actuators.

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Area of Science:

  • Materials Science
  • Control Engineering
  • Artificial Intelligence

Background:

  • Hysteresis in magnetic shape memory alloy (MSMA) actuators hinders their practical applications.
  • Accurate modeling of MSMA actuator hysteresis is crucial for effective control.

Purpose of the Study:

  • To develop a novel hysteresis model for MSMA actuators using radial basis function neural networks (RBFNN).
  • To design an inverse RBFNN model for hysteresis compensation and compare it with traditional methods.
  • To implement a feed-forward controller based on the inverse model to achieve high-precision control of MSMA actuators.

Main Methods:

  • Development of a hysteresis model utilizing radial basis function neural networks (RBFNN).
  • Creation of an inverse RBFNN model for hysteresis compensation.
  • Comparison of the RBFNN inverse model with the traditional cut-and-try method.
  • Implementation of a feed-forward controller using the inverse hysteresis model.

Main Results:

  • The RBFNN-based inverse hysteresis model achieved a maximum modeling error of 0.79%.
  • The maximum modeling error was reduced by 1.85% compared to the traditional cut-and-try method.
  • The feed-forward controller demonstrated a maximum tracking error rate of 0.38%.

Conclusions:

  • The proposed RBFNN hysteresis model effectively describes and compensates for hysteresis in MSMA actuators.
  • The inverse RBFNN model offers superior accuracy compared to traditional methods for hysteresis modeling.
  • The feed-forward controller successfully reduces hysteresis and achieves high-precision control of MSMA actuators.