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A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
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HSMS-Based Event-Triggered Adaptive Dynamic Programming for Pursuit-Evasion Differential Games of Multiagent Systems.

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    Summary
    This summary is machine-generated.

    This study presents an optimal control strategy for multiagent systems in pursuit-evasion games. It uses adaptive dynamic programming and a novel target allocation algorithm to improve coordination and resource efficiency.

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    Area of Science:

    • Robotics and Control Systems
    • Game Theory
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) face complex coordination challenges in pursuit-evasion differential games (PEDGs).
    • Existing control strategies often struggle with communication overhead and suboptimal resource allocation.
    • Ensuring cohesive team behavior while managing individual agent objectives is critical.

    Purpose of the Study:

    • To develop a distributed approximate optimal control scheme for multiagent pursuit-evasion games.
    • To enhance coordination and communication efficiency in multiagent systems.
    • To address the challenge of dynamic target allocation among pursuer agents.

    Main Methods:

    • Formulation of agent interactions using a divide-and-conquer algebraic graph approach.
    • Implementation of a state event-triggered mechanism (ETM) for communication resource conservation.
    • Design of a polymeric hierarchical sliding mode surface (HSMS) for improved system response.
    • Development of a novel dynamic target allocation algorithm for rational pursuer assignment.
    • Application of adaptive dynamic programming (ADP) with a single-critic neural network (NN) to solve Hamilton-Jacobi-Bellman (HJB) equations for optimal control policies.

    Main Results:

    • The proposed HSMS-based event-triggered optimal control policies effectively manage the PEDGs problem in MASs.
    • The dynamic target allocation algorithm enhances team coordination among pursuers.
    • The state event-triggered mechanism significantly conserves communication resources.
    • Simulations demonstrate the effectiveness of the control scheme in a two-pursuer-two-evader scenario.

    Conclusions:

    • The integrated approach combining HSMS, ETM, and ADP provides a robust solution for distributed optimal control in PEDGs.
    • The study validates the efficacy of the proposed methods in improving coordination, resource management, and overall system performance.
    • This research contributes to the advancement of intelligent control strategies for complex multiagent systems.