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Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems.

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This study introduces a novel adaptive neural network control for nonlinear systems, ensuring fixed-time convergence for system states. It offers a practical procedure for industrial applications.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Nonlinear interconnected systems present significant control challenges due to uncertainties.
  • Achieving rapid and guaranteed convergence times is crucial for many industrial processes.
  • Existing control methods may struggle with unknown dynamics and fixed-time requirements.

Purpose of the Study:

  • To propose a novel adaptive fixed-time neural network tracking control scheme.
  • To address unknown system uncertainties within a fixed-time framework.
  • To provide a robust control solution for nonlinear interconnected systems.

Main Methods:

  • Utilizing an adaptive backstepping technique to handle unknown system uncertainties.
  • Employing neural networks for real-time identification of these uncertainties.
  • Applying Lyapunov stability analysis to guarantee fixed-time convergence.

Main Results:

  • Demonstrated that all system states converge to small regions around zero.
  • Achieved convergence within a fixed-time, outperforming traditional methods.
  • Validated the control scheme's effectiveness through simulation examples.

Conclusions:

  • The proposed adaptive fixed-time neural network control scheme is effective for nonlinear systems.
  • The method ensures rapid and predictable convergence for system states.
  • A step-by-step procedure is provided for practical implementation in industry.