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A distributed time-varying neurodynamic algorithm for multi-UAV collaborative target tracking problem in maritime

Lingxi Zhang1, Xing He1, Junzhi Yu2

  • 1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

ISA Transactions
|September 10, 2025
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Summary

This study introduces a new neurodynamic algorithm for multiple unmanned aerial vehicles (UAVs) to track targets in maritime search and rescue, even with changing wind speeds. The system effectively tracks targets regardless of their trajectory variations.

Keywords:
Distributed time-varying neurodynamic algorithmFixed-time convergenceMaritime search and rescueMultiple unmanned aerial vehiclesTarget tracking

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Area of Science:

  • Robotics and Control Systems
  • Optimization Theory
  • Maritime Operations

Background:

  • Maritime search and rescue operations often rely on unmanned aerial vehicles (UAVs) for target tracking.
  • Existing methods typically assume fixed environmental conditions, such as constant wind speed, limiting their real-world applicability.
  • Collaborative tracking by multiple UAVs offers enhanced coverage and robustness but faces challenges with dynamic environments.

Purpose of the Study:

  • To develop and analyze a distributed algorithm for collaborative target tracking by multiple UAVs under time-varying (TV) conditions, specifically addressing varying wind speeds.
  • To investigate the performance of the proposed algorithm in scenarios with dynamic target trajectories.
  • To ensure fixed-time convergence for the tracking algorithm, independent of initial conditions.

Main Methods:

  • Formulation of a class of TV convex optimization problems with inequality constraints.
  • Design of a distributed TV neurodynamic algorithm integrating prediction-correction and sliding mode control.
  • Theoretical analysis using Lyapunov functions to prove fixed-time convergence properties.
  • Experimental validation using established 3D target trajectory equations with sinusoidal variations.

Main Results:

  • The proposed distributed TV neurodynamic algorithm achieves fixed-time convergence for collaborative target tracking.
  • The algorithm's performance in tracking efficiency remains robust despite TV target trajectories.
  • Experimental results confirm the system's effectiveness under varying wind speed conditions.

Conclusions:

  • The developed algorithm provides a robust solution for multi-UAV collaborative target tracking in dynamic maritime environments.
  • Fixed-time convergence ensures reliable and predictable system behavior, crucial for search and rescue missions.
  • The system's resilience to TV target trajectories and wind conditions enhances its practical utility.