Adaptive Implicit Inverse Control for a Class of Butterfly-Like Hysteretic Nonlinear Systems and Its Application to Dielectric Elastomer Actuators

Yue Wang, Xiuyu Zhang, Zhi Li, Xinkai Chen, Chun Yi Su

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a butterfly-like Prandtl-Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small L norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator (DEA) based motion control platform.

Original languageEnglish
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Adaptation models
  • Density functional theory
  • Hysteresis
  • Neural networks
  • Nonlinear systems
  • Physics
  • Predictive models
  • Robust adaptive control
  • butterfly-like hysteresis
  • dielectric elastomer actuator
  • implicit inverse

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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