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

This article proposes a game-based distributed decision optimization method for heterogeneous multiagent systems with unknown nonlinear dynamics. Due to the information exchange between agents in the network, the unknown nonlinear dynamics lead to the degradation of local and all-agent control performance, which causes the strategies of all agents to deviate from the Nash equilibrium under a given goal. To address this problem, an adaptive distributed algorithm is designed to seek Nash equilibrium by combining two optimization levels. Specifically, the decision layer uses a distributed consensus algorithm to achieve benefit evaluation and a gradient algorithm to generate reference signals. Then, the control layer uses the virtual reference signal from the decision layer and the neural network estimation information to design an adaptive control algorithm. The proposed method performs real-time adaptive optimization of the strategies and control performance of the decision and control layers, ensuring the successful implementation of the distributed Nash equilibrium search. The convergence of the proposed algorithm is proved in the Lyapunov sense. Finally, simulation examples demonstrate the performance and effectiveness of the proposed method.