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Updated: Jan 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Neural network-based practical prescribed time adaptive tracking control for nonlinear networked control systems

Ruonan Liu1, Guangdeng Zong2, Xudong Zhao3

  • 1Interdisciplinary Center, Shandong University, Jinan, 250100, China; School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a neural network control method for nonlinear networked systems facing deception attacks. The approach ensures tracking errors stay within bounds in a set time, enhancing system performance and stability.

Keywords:
Adaptive tracking controlDeception attacksNetworked control systemsNeural networkPractical prescribed time control

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Networked Systems

Background:

  • Networked control systems are vulnerable to deception attacks.
  • Ensuring tracking performance under attacks is challenging.
  • Prescribed-time control offers enhanced transient and steady-state guarantees.

Purpose of the Study:

  • To develop a neural network-based adaptive tracking control for nonlinear networked systems under deception attacks.
  • To ensure tracking errors remain within prescribed bounds within a finite, specified time.
  • To address sensor and actuator deception attacks effectively.

Main Methods:

  • Constructing an attack compensator using neural networks and compromised states.
  • Introducing a practical prescribed-time function to bound tracking errors.
  • Designing a first-order sliding mode differentiator to avoid complexity explosion.

Main Results:

  • All signals in the closed-loop system are proven to be bounded.
  • Tracking errors converge to a predetermined boundary within the prescribed time.
  • The proposed control algorithm demonstrates effectiveness in numerical and robotic arm examples.

Conclusions:

  • The developed neural network control effectively handles deception attacks in nonlinear networked systems.
  • The prescribed-time approach guarantees robust tracking performance and system stability.
  • The method offers a practical solution for secure and efficient control applications.