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Predefined-time adaptive neural network decentralized control for large-scale interconnected systems with input

Xiaoli Li1, Guoju Zhang2, Yingshan Zhou2

  • 1School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China.

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|January 28, 2025
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Summary

This study introduces a new adaptive neural network controller for nonlinear systems with hysteresis, ensuring fast and accurate tracking within a set time. The method overcomes complexity and improves control precision for interconnected systems.

Keywords:
Adaptive neural network decentralized controllerInput hysteresisLarge-scale interconnected systemModified command filterPredefined-time stability

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Area of Science:

  • Control Theory
  • Artificial Intelligence
  • Nonlinear Systems

Background:

  • Large-scale interconnected nonlinear systems present significant control challenges.
  • Input hysteresis and system uncertainties degrade control performance.
  • Existing backstepping methods can suffer from complexity explosion and chattering.

Purpose of the Study:

  • To develop a predefined-time adaptive neural network decentralized controller.
  • To address large-scale interconnected nonlinear systems with input hysteresis.
  • To guarantee tracking error convergence within a specified settling time.

Main Methods:

  • Utilized backstepping technique combined with a modified command filter.
  • Employed an online neural network approximator for system uncertainties.
  • Introduced a novel predefined-time error compensation mechanism.

Main Results:

  • Tracking error converges to a small bounded set within a predefined settling time.
  • The control parameter adjusts the convergence time.
  • Effectively mitigated complexity explosion and chattering phenomena.
  • Neural networks compensated for system uncertainties.

Conclusions:

  • The proposed controller is feasible and effective for nonlinear systems with hysteresis.
  • Achieved precise control with guaranteed convergence time.
  • Offers a robust solution for complex interconnected systems.