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Neural network-based dynamic target enclosing control for uncertain nonlinear multi-agent systems over signed

Weihao Li1, Jiangfeng Yue1, Mengji Shi1

  • 1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China; Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu, 611731, Sichuan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 25, 2024
PubMed
Summary

This study introduces a neural network approach for robust target enclosing control in multi-agent systems. The method enhances prediction accuracy and control robustness, even with uncertain target dynamics and agent disturbances.

Keywords:
Matched/unmatched disturbancesMulti-agent systemsNeural networksSigned networksTarget enclosing

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Networked Systems

Background:

  • Multi-agent systems require robust control for tasks like target enclosing.
  • Estimating uncertainty dynamics is crucial for accurate predictions and control.
  • Traditional methods may require expensive sensors for high-order target information.

Purpose of the Study:

  • To develop a neural network-based method for uncertain target enclosing control.
  • To enhance control robustness in multi-agent systems over signed networks.
  • To reduce reliance on high-cost sensors for target state estimation.

Main Methods:

  • Constructing a nominal target enclosing controller using bipartite consensus error.
  • Employing neural network approximation to estimate uncertain target dynamics and agent disturbances.
  • Generating feedforward control components based on estimated uncertainties.

Main Results:

  • Achieved accurate target-enclosing control despite uncertain dynamics and disturbances.
  • Demonstrated improved robustness against matched and unmatched disturbances.
  • Eliminated the need for high-cost sensors to obtain target velocity and acceleration.

Conclusions:

  • The proposed neural network-based controller effectively addresses uncertain target enclosing control.
  • The method enhances robustness and accuracy in multi-agent systems.
  • It offers a cost-effective solution by avoiding expensive sensor requirements.