In a classical distributed framework, we present a novel distributed observer for nonlinear continuous-time plants. A network of sensors monitors a Multi-Inputs- Multi-Outputs plant. Each sensor measures only a portion of the plant’s outputs and the sensing capability is different from sensor to sensor. The standing assumption of strongly connected digraph on the underlying sensor network ensures robustness and direct communication paths between nodes. Moreover, incremental homogeneity assumptions on the plant establishes a very large class of nonlinear systems for which a distributed observer can be designed. The distributed observer consists of a local observers associated with each sensor, which estimate asymptotically the entire state of the plant only by using the local sensing capability and information exchanged through the communication network.

Distributed estimation for nonlinear systems

2019

Abstract

In a classical distributed framework, we present a novel distributed observer for nonlinear continuous-time plants. A network of sensors monitors a Multi-Inputs- Multi-Outputs plant. Each sensor measures only a portion of the plant’s outputs and the sensing capability is different from sensor to sensor. The standing assumption of strongly connected digraph on the underlying sensor network ensures robustness and direct communication paths between nodes. Moreover, incremental homogeneity assumptions on the plant establishes a very large class of nonlinear systems for which a distributed observer can be designed. The distributed observer consists of a local observers associated with each sensor, which estimate asymptotically the entire state of the plant only by using the local sensing capability and information exchanged through the communication network.
26-feb-2019
Inglese
BATTILOTTI, Stefano
valutatori esterni: C. Manes
A. Tornambé
ORIOLO, Giuseppe
Università degli Studi di Roma La Sapienza
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/134648
Il codice NBN di questa tesi è URN:NBN:IT:UNIROMA1-134648