Event-Based Robust Adaptive Distributed Observer Under Directed Graphs
针对有噪声环境下的有向图网络，提出一种事件触发自适应分布式观测器，各智能体仅利用局部含噪测量值协同估计目标状态，无需全局信息，并证明估计误差有界且无Zeno现象。
This article focuses on the event-based fully distributed state estimation problem under noisy environment and directed graphs. In a networked system, agents cooperatively estimate the target system state disturbed by process noise. However, due to the existence of measurement noise, each agent can only access partial and disturbed measurement output information. Meanwhile, given the limitations of resources in the networked system and the difficulty of acquiring global information, the event-based robust adaptive distributed observer is proposed. Specifically, by introducing robust adaptive coupling gains, a fully distributed design is achieved, eliminating the dependence on global information. Through the design of event-triggered mechanism, an event-based communication manner is implemented to reduce communication and energy resource consumption. Moreover, in view of the heterogeneity of the undetectable subspace and the differences in parameter design among agents, this article introduces a coordinate transformation and constructs a new Lyapunov function to further analyze the stability of the estimation error. Then, theoretical analysis shows that during cooperative estimation under directed graphs, the norm of estimation error can asymptotically converge to a compact set without Zeno behavior. Finally, a simulation example is provided to verify the effectiveness of the proposed event-based robust adaptive distributed observer.