Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics.

Area of Science:

• Control Engineering
• Distributed Systems
• Game Theory

Background:

• Heterogeneous multiagent systems face performance degradation due to unknown nonlinear dynamics and information exchange.
• Deviations from Nash equilibrium occur, impacting overall system control and decision-making.
• Existing methods struggle to maintain optimal performance in such complex, dynamic environments.

Purpose of the Study:

• To propose a game-based distributed decision optimization method for heterogeneous multiagent systems.
• To address the challenges posed by unknown nonlinear dynamics and information exchange.
• To achieve and maintain Nash equilibrium for improved control performance.

Main Methods:

• An adaptive distributed algorithm combining two optimization levels: decision and control layers.
• Decision layer utilizes distributed consensus and gradient algorithms for benefit evaluation and reference signal generation.
• Control layer employs a neural network-based adaptive control algorithm using virtual reference signals.

Main Results:

• The method enables real-time adaptive optimization of strategies and control performance.
• Successful implementation of distributed Nash equilibrium search is demonstrated.
• Convergence of the proposed algorithm is proven using Lyapunov stability analysis.

Conclusions:

• The proposed game-based distributed optimization method effectively handles unknown nonlinear dynamics in multiagent systems.
• The adaptive algorithm ensures agents converge to a Nash equilibrium, enhancing system performance.
• Simulation results validate the method's effectiveness and robustness.