Cooperative Differential Game-Based Distributed Optimal Synchronization Control of Heterogeneous Nonlinear Multiagent...

Area of Science:

• Control Theory
• Artificial Intelligence
• Systems Engineering

Background:

• Distributed synchronization is crucial for multiagent systems (MASs).
• Traditional methods struggle with nonlinear dynamics and limited information exchange.
• Reinforcement learning (RL) offers a promising approach for adaptive control.

Purpose of the Study:

• To develop an online, off-policy reinforcement learning (RL) algorithm for optimizing distributed synchronization in nonlinear multiagent systems (MASs).
• To address challenges posed by limited leader information using adaptive neural network observers.
• To provide a robust and real-time solution for complex MAS coordination.

Main Methods:

• Designed a novel adaptive model-free observer using neural networks (NNs) to estimate leader information.
• Formulated an augmented system and a distributed cooperative performance index.
• Transformed the synchronization problem into solving the Hamilton-Jacobian-Bellman (HJB) equation.
• Proposed an online, off-policy policy iteration (PI) algorithm for real-time optimization.
• Developed a novel mathematical analysis for proving algorithm stability and convergence.

Main Results:

• Successfully designed and proved the feasibility of the neural network-based observer.
• Established the stability and convergence of the proposed online off-policy PI algorithm.
• Demonstrated the algorithm's effectiveness in optimizing distributed synchronization via simulation.
• The offline on-policy algorithm served as a precursor for stability analysis.

Conclusions:

• The developed online off-policy RL algorithm effectively optimizes distributed synchronization in nonlinear MASs.
• The novel observer design and mathematical analysis provide a robust framework for real-time MAS control.
• Simulation results validate the theoretical findings and practical applicability of the proposed method.