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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Uniform-performance-constrained fixed-time neuro-control for stochastic nonlinear systems under dynamic event

Wenjie Si1, Xunde Dong2, Feifei Yang3

  • 1School of Electrical and Control Engineering, Henan University of Urban Construction, Longxiang Avenue, Xincheng District, Pingdingshan, 467036, Henan, China.

ISA Transactions
|January 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fixed-time control strategy for stochastic nonlinear systems using event-triggered communication. The method ensures fast convergence and steady-state accuracy while minimizing communication load.

Keywords:
Command filterDynamic event threshold settingFixed-time convergenceStochastic nonlinear systemsTracking error performance constraints

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Area of Science:

  • Control Theory
  • Nonlinear Systems
  • Stochastic Systems

Background:

  • Stochastic nonlinear systems present challenges in control due to uncertainties and performance constraints.
  • Event-triggered communication is crucial for reducing communication load in control systems.
  • Achieving fixed-time stability with guaranteed performance is a key objective in advanced control.

Purpose of the Study:

  • To develop a practical fixed-time control strategy for stochastic nonlinear systems with event-triggered communication.
  • To address uniform tracking error performance constraints and ensure fast convergence.
  • To mitigate computational complexity and communication overhead.

Main Methods:

  • Utilizing an improved performance function combined with an asymmetric barrier Lyapunov function for enhanced tracking.
  • Applying practical fixed-time stability with command filtering and error compensation mechanisms.
  • Designing dynamic event-triggering mechanisms with adjustable thresholds and RBF neural networks for uncertainty estimation.

Main Results:

  • The proposed controller ensures system error converges to zero in a fixed time.
  • System output remains within preset boundaries, guaranteeing boundedness of all signals.
  • The control strategy effectively handles system uncertainties and avoids singularity issues.

Conclusions:

  • The developed event-triggered fixed-time control is effective for stochastic nonlinear systems.
  • The approach offers improved convergence speed, steady-state accuracy, and reduced communication load.
  • Simulation studies validate the practical applicability and superiority of the proposed control method.