Nash Equilibrium Strategies for Multicluster Pursuit-Evasion Game With Disturbances: A Prescribed-Time Convergence...

Area of Science:

• Robotics and Control Systems
• Game Theory
• Distributed Systems

Background:

• Multi-agent systems face challenges in achieving coordinated goals under uncertainty.
• Pursuit-evasion games (PEGs) are complex scenarios requiring strategic decision-making.
• External disturbances can significantly impact the stability and convergence of multi-agent systems.

Purpose of the Study:

• To develop a control algorithm for achieving prescribed-time Nash equilibrium (NE) in multicluster pursuit-evasion games (PEGs).
• To address the challenge of external disturbances in achieving timely convergence to NE.
• To enable collaborative pursuit and evasion strategies within a user-defined timeframe.

Main Methods:

• Design of a prescribed-time disturbance observer (PTDO) to estimate and compensate for external disturbances.
• Development of a novel collaborative control algorithm for multiple pursuers and evaders.
• Theoretical analysis to guarantee prescribed-time convergence to the Nash equilibrium.
• Numerical simulations to validate algorithm performance and convergence time flexibility.

Main Results:

• The proposed PTDO effectively estimates and compensates for external disturbances.
• The novel control algorithm ensures all pursuers converge to the Nash equilibrium within the specified prescribed time.
• Simulations confirm the algorithm's effectiveness across various initial conditions.
• The user-defined convergence time is shown to be flexible and adjustable.

Conclusions:

• The developed control strategy successfully achieves prescribed-time Nash equilibrium in disturbed multicluster pursuit-evasion games.
• The integration of PTDO enhances robustness against external disturbances.
• The algorithm offers a flexible and effective solution for coordinated multi-agent tasks with time constraints.