Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling
For control systems engineers, this work provides a method to handle communication constraints and packet loss in distributed sensor networks, though it is an incremental extension of existing LMI-based observer design techniques.
The paper addresses continuous-time state observation over lossy networks with round-robin scheduling, proposing an LMI framework for designing observer gains that ensure asymptotic stability of error dynamics.
In this paper, we study the problem of continuous-time state observation over lossy communication networks. We consider the situation in which the samplers for measuring the output of the plant are spatially distributed and their communication with the observer is scheduled according to a round-robin scheduling protocol. We allow the observer gains to dynamically change in synchronization with the scheduling of communications. In this context, we propose a linear matrix inequality (LMI) framework to design the observer gains that ensure the asymptotic stability of the error dynamics in continuous time. We illustrate the effectiveness of the proposed methods by several numerical simulations.