信息约束下的博弈学习动力学及其在协同控制中的应用

基于博弈学习的协同控制是博弈学习和协同控制结合而形成的新型交叉研究领域，它以博弈模型刻画协同控制问题，利用博弈学习动力学设计协同控制算法，为解决各类工程任务中的协同控制问题提供了一个新的研究框架。目前，博弈学习研究主要基于完全信息条件，而实际工程应用中的信息约束是制约博弈学习理论在协同控制问题中应用的核心难点。本课题从网络信息约束与最小信息约束等两类典型信息约束出发，利用网络科学与最优化方法相关理论与工具，对信息约束下的博弈学习动力学构造、分析与应用展开系统的探索：建立若干信息约束下博弈学习动力学的构造方法；阐明不同博弈学习动力学所适用的博弈类型以及收敛效率；设计基于信息约束下博弈学习动力学的协同控制算法，并考察其在各类协同控制问题中应用。本课题的实现，能够从信息约束的新视角丰富博弈学习理论的内容、扩大其在协同控制问题中的应用范围，并为理解复杂系统中的集群决策行为提供新的理论与技术支持。

Game-based cooperative control is a new interdisciplinary research field at the cross-point of game learning and cooperative control. With a game model representing the problem of cooperative control, and using game learning dynamics to design cooperative control algorithms, the game-based cooperative control provides a new framework for solving the tasks of cooperative control in various applications. Current studies on game learning dynamics are mainly based on the complete information assumption, and information limitation is the core challenge in application of game learning dynamics in cooperative control problems. In this proposal, beginning with two typical information limitations, including networked information and minimal information, and by using analysis tools of network theory and optimization methods, systematic investigations will be carried out on design, analysis, and applications of game learning dynamics with limited information. In detail, some design methods of game learning dynamics with limited information will be established; and the applicable game types and convergence performances of the learning dynamics will be clarified. Furthermore, cooperative control algorithms will be designed and applied to different kinds of cooperative control problems based on the game learning dynamics with limited information. Completeness of this project will enrich the content of game theory, enhance the applications of game learning in cooperative control, and provide a new theoretical and technical perspective for understanding the collective decision-making process in complex systems.