Active-Passive Dynamic Consensus Filters for Linear Time-Invariant Multiagent Systems

Active-passive dynamic consensus filters consist of a group of agents, where a subset of these agents are able to observe a quantity of interest (i.e. active agents) and the rest are subject to no observations (i.e. passive agents). Specifically, the objective of these filters is that the states of all agents are required to converge to the weighted average of the set of observations sensed by the active agents. Existing active-passive dynamic consensus filters in the classical sense assume that all agents can be modeled as having single integrator dynamics, which may not always hold in practice. Motivating from this standpoint, the contribution of this paper is to introduce a new class of active-passive dynamic consensus filters, where agents have (homogeneous) linear time-invariant dynamics. We demonstrate that for output controllable agents, the output of all active and passive agents converge to a neighborhood of the weighted average of the set of applied exogenous inputs. A numerical example is also given to illustrate the efficacy of the presented theoretical results.