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

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.