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Unformatted text preview: 794 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 54, NO. 4, APRIL 2009 Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach Riccardo M. G. Ferrari , Student Member, IEEE , Thomas Parisini , Senior Member, IEEE , and Marios M. Polycarpou , Fellow, IEEE Abstract— This technical note deals with the problem of designing a dis- tributed fault detection methodology for distributed (and possibly large- scale) nonlinear dynamical systems that are modelled as the interconnec- tion of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive ap- proximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among dif- ferent subsystems. Simulation results provide an evidence of the effective- ness of the proposed distributed fault detection scheme. Index Terms— Adaptive estimation, distributed detection, fault diag- nosis, large-scale systems, nonlinear systems. I. INTRODUCTION The problem of automated fault diagnosis and accommodation is motivated by the need to develop more autonomous and intelligent sys- tems that operate reliably in the presence of faults. In dynamical sys- tems, faults are characterized by critical and unpredictable changes in the system dynamics, thus requiring the design of suitable fault diag- nosis schemes . Moreover, with current technological trends, sev- eral systems of practical interest are large-scale and/or physically dis- tributed and thus the decomposition and spatial distribution of highly demanding computational tasks is of critical importance. Recently there has been significant research activity in modeling, control and cooperation methodologies for distributed systems (see, for example, , and the references cited therein). This activity is mo- tivated by several applications, especially in complex large-scale sys- tems, such as traffic networks, environmental systems, communication networks, power grid networks, water distribution networks, etc. Such systems, although their dynamics and control objectives may appear to be completely different, have some important common characteristics: their dynamics are complex and spatially distributed, and, as a result, it is typically more convenient to decompose the system into smaller subsystems which can be controlled locally (or regionally). The study of controlling spatially distributed systems is not a new problem. As Manuscript received June 26, 2007; revised May 15, 2008. Current version published April 08, 2009. This work was supported in part by the Italian Min-published April 08, 2009....
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