不确定环境下多自主体系统的分布式估计与控制

多自主体系统是当前系统控制界研究的热点问题. 在实际中, 自主体系统通常并不是在理想的环境下执行任务, 而是面临多源头、多层次和多变化的各类不确定性因素的影响. 它们通过在微观层面上影响各自主体决策的正确性, 从而在宏观上对自主体系统的整体行为产生显著影响. 不确定性因素和多自主体系统分布式信息架构交互耦合, 给系统的设计与分析带来本质性困难. 本文围绕分布式估计与分布式控制问题, 研究在随机通信噪声、数据丢失、量化和系统未知结构参数等不确定因素影响下, 如何为各自主体设计更加鲁棒、更加有效的分布式估计算法及分布式控制律, 以实现全局估计与控制目标, 并对闭环系统性能进行系统分析.

Distributed estimation and control of multi-agent systems in uncertain environment

Due to the wide applications in wireless sensor networks, multi-robot cooperative control and satellite formation flying, etc., distributed control and estimation of multi-agent systems (MASs) are now a hot topic in the system control community. In practice, when carrying out distributed tasks, MASs are often subject to various uncertainties, which can make significant macro-influence to the whole systems via affecting each agent''s microscopic decisions. The interaction among uncertainties and distributed information pattern may bring essential difficulty to the design and analysis of MASs. This paper is aimed at seeking more robust and more effective distributed estimation and control protocols to achieve global estimation and control tasks under various uncertainties including stochastic noises, signal loses, quantization errors and unknown system structures, etc. Specifically, distributed estimation, distributed control and distributed adaptive control are investigated.