In this work, a novel multi-agent framework for cooperative supervisory estimation of linear time-invariant (LTI) systems is proposed. This framework is developed based on the notion of sub-observers and a discrete-event system (DES) supervisory control and is applicable to a large class of systems. We introduce a group of sub-observers where each sub-observer is estimating certain states that are conditioned on a given input, output, and state information. The cooperation among the subobservers is managed by a DES supervisor. The supervisor makes decisions regarding the selection and configuration of a set of sub-observers to successfully estimate all the system states, while the feasibility of the overall integrated cooperative sub-observers is verified. When certain anomalies (faults) are present in the system, or the sensors and sub-observers become unreliable, the supervisor reconfigures the set of selected sub-observers so that the impacts of anomalies on the estimation performance are minimized to the extent that is possible. The application of our proposed methodology in a practical industrial process is demonstrated through numerical simulations.

In this paper, a novel framework for networked estimation of multi-agent systems subject to presence of actuator faults is proposed. This framework is developed based on the notion of sub-observers where within a group of sub-observers each sub-observer estimates certain states that are conditioned on a given input, output, and other state information. We model the overall estimation process by a weighted estimation (WE) digraph. By selecting an appropriate path in the WE digraph, an assigned supervisor can select and configure a set of sub-observers to successfully estimate all the system states. In the presence of large intermittent disturbances, noise, and faults certain sub-observers may become invalid, and consequently the supervisor reconfigures the set of sub-observers by selecting a new path in the estimation digraph such that the impacts of these uncertainties are confined to only the local estimators. This will prevent the propagation of uncertainties on the estimation performance of the entire multi-agent system. Simulation results provided for a five satellite formation flight system in deep space confirm the validity and applicability of our proposed analytical work.

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