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Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Prescribed-Time Leader-Follower Synchronization of Higher-Order Nonlinear Multi-Agent Systems via Fuzzy Neural

Safeer Ullah1, Muhammad Zeeshan Babar2, Sultan Alghamdi3,4

  • 1Department of Electrical Engineering, Quaid-e-Azam College of Engineering & Technology, Sahiwal 57000, Pakistan.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new control framework for faster, robust synchronization of nonlinear multi-agent systems (MAS) within a set time. It uses fuzzy neural networks and adaptive control to handle uncertainties and disturbances effectively.

Keywords:
adaptive robust controlfuzzy neural networksleader–follower consensusmulti-agent systemsnon-singular terminal sliding mode controlprescribed-time synchronization

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Area of Science:

  • Control Theory
  • Artificial Intelligence
  • Systems Engineering

Background:

  • Multi-agent systems (MAS) synchronization is crucial for coordinated behaviors.
  • Existing methods struggle with prescribed-time control under uncertainties.

Purpose of the Study:

  • To develop a novel control framework for prescribed-time synchronization of nonlinear MAS.
  • To ensure leader-follower consensus within a user-defined time, irrespective of initial states.

Main Methods:

  • Integration of fuzzy neural networks (FNN) for online nonlinearity approximation.
  • Utilizing a robust non-singular terminal sliding mode controller (NTSMC).
  • Employing adaptive update laws and Lyapunov stability analysis.

Main Results:

  • Achieved prescribed-time synchronization for higher-order nonlinear MAS.
  • Demonstrated enhanced robustness against parametric uncertainties and external disturbances.
  • Simulation confirmed faster convergence and adaptability compared to conventional methods.

Conclusions:

  • The proposed FNN-NTSMC framework offers superior performance for MAS synchronization.
  • Effective handling of uncertainties and disturbances ensures reliable consensus.
  • The method provides precise control within a user-defined timeframe.