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Related Experiment Videos

Fixed-time adaptive neural network compensation control for uncertain nonlinear systems.

Jiahua Ma1, Zhikai Yao2, Wenxiang Deng1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2025
PubMed
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This study introduces a novel fixed-time adaptive neural network control method to enhance the performance of nonlinear systems with uncertainties. The approach ensures fixed-time stability, overcoming control limitations in complex systems.

Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Artificial Intelligence in Control

Background:

  • Uncertainties in nonlinear systems hinder control performance.
  • High-order nonlinear systems present significant control challenges.
  • Existing methods may suffer from conservatism or differential explosion.

Purpose of the Study:

  • To develop a fixed-time adaptive neural network compensation control method.
  • To address both uncertain nonlinearities and parametric uncertainties.
  • To improve control performance and ensure fixed-time stability.

Main Methods:

  • Designed a fixed-time adaptive neural network (FTANN) for uncertain nonlinearities.
  • Developed a new fixed-time adaptive law for parametric uncertainties.
Keywords:
AdaptationFixed-time controlNeural networkUncertain nonlinear systems

Related Experiment Videos

  • Integrated FTANN and adaptive law with a gain-adaptive fixed-time filter within the dynamic surface control (DSC) framework.

    • Main Results:

    • The proposed controller guarantees fixed-time stability for all system states, as proven by Lyapunov analysis.
    • The method resolves the "differential explosion" problem inherent in some control designs.
    • Reduced controller conservatism by lowering the robust feedback gain.

    Conclusions:

    • The novel fixed-time adaptive neural network control method effectively manages uncertainties in high-order nonlinear systems.
    • The integrated approach ensures system stability within a fixed time.
    • Simulation and experimental results validate the controller's superior performance.