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Adaptive Fixed-Time Neural Networks Control for Pure-Feedback Non-Affine Nonlinear Systems with State Constraints.

Yang Li1, Quanmin Zhu2, Jianhua Zhang1

  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China.

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|May 28, 2022
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Summary
This summary is machine-generated.

This study introduces a novel fixed-time adaptive neural network control strategy for non-affine nonlinear systems. The new method ensures system output tracks desired signals within a guaranteed fixed time, independent of initial conditions.

Keywords:
adaptive controlneural network controlnon-affine nonlinear systemsnonlinear constraint systemspure feedback

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Area of Science:

  • Control Theory
  • Nonlinear Systems
  • Artificial Intelligence

Background:

  • Pure-feedback non-affine nonlinear systems present significant control challenges due to their complex dynamics and state constraints.
  • Existing control strategies often struggle to guarantee performance within a finite, predetermined time frame.

Purpose of the Study:

  • To develop a novel fixed-time adaptive neural network control strategy for pure-feedback non-affine nonlinear systems with state constraints.
  • To ensure the system's output signal achieves precise tracking of the desired signal within a fixed time.
  • To demonstrate that the control design's settling time is independent of the system's initial states.

Main Methods:

  • Adaptive backstepping technology was employed to design Lyapunov functions for each subsystem.
  • Neural networks were utilized for real-time identification of unknown system parameters.
  • A feedback control signal based on the error system was developed.

Main Results:

  • The proposed control strategy guarantees that the system's output signal tracks the reference signal within a fixed time.
  • Stability analysis confirmed the convergence of the tracking error within a fixed time.
  • The upper bound of the settling time can be adjusted by modifying controller parameters and adaptive laws, independent of initial conditions.

Conclusions:

  • The developed fixed-time adaptive neural network control strategy effectively addresses control challenges in pure-feedback non-affine nonlinear systems.
  • The strategy ensures finite-time convergence of tracking errors, offering improved performance and predictability.
  • The controller's settling time independence from initial conditions simplifies practical implementation and enhances robustness.