Topological Identification in Networks of Dynamical Systems

Materassi, Donatello; Innocenti, Gabbriella

doi:10.1109/tac.2010.2042347

Cited by 171 publications

(98 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One should mention that the problem of detecting topological changes can also be cast as a topology identification problem; for example, see Materassi and Innocenti (2010); Sanandaji et al (2011);Chowdhary et al (2011). In fact, detection and identification are certainly closely connected problems.…”

Section: Introductionmentioning

confidence: 99%

Indiscernible topological variations in DAE networks with applications to power grids

et al. 2017

View full text Add to dashboard Cite

The ability to detect topology variations in dynamical networks defined by differential algebraic equations (DAEs) is considered. We characterize the existence of initial states, for which topological changes are indiscernible. A key feature of our characterization is the ability to verify indiscernibility just in terms of the nominal topology. We apply the results to a power grid model and also discuss the relationship to recent mode-detection results for switched DAEs.

show abstract

Section: Introductionmentioning

confidence: 99%

Indiscernible topological variations in DAE networks with applications to power grids

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Early work addressing this problem can be found in [9] and [1]. Some recent work on topology identification of networked systems can be found in [18], [19], [23]. If the network is sparsely interconnected, regularization ideas could be applied, see [20] and [2] .…”

Section: Introductionmentioning

confidence: 99%

On optimal input design for networked systems

Hägg

Wahlberg

2015

Automatica

View full text Add to dashboard Cite

The topic of this paper is optimal input signal design for identification of interconnected/networked dynamic systems. We consider the case when it is only possible to design some of the input signals, while the rest of the inputs are only measurable. This is most common in industrial applications, where external excitation can only be applied to some subsystems. One example is feed-forward control from measurable disturbances. The optimal input signal will be correlated with the measured signals. The main purpose of this paper is to reveal how to re-formulate the input design problem for networked systems as an input design problem for feedback control systems. We can then use the powerful partial correlation approach for optimal closed loop input design. This means that the corresponding networked optimal input design problem can be formulated as a semi-definite program, for which there are efficient numerical methods. We evaluate this approach using two numerical examples with important applications. The result reveals some non-trivial interesting properties of the optimal input signals.

show abstract

“…However currently interest has been renewed in trying to detect the topology of more complex networks based on measurements of the system (Friedman et al [2010], Yuan et al [2010], Sanandaji et al [2011], Materassi and Innocenti [2010], Materassi et al [2011] among many others).…”

Section: Introductionmentioning

confidence: 99%

“…Most often nonparametric or FIR output error models are used to model the dynamics of the links G ij (q, θ) Friedman et al [2010], Yuan et al [2010], Sanandaji et al [2011], Materassi and Innocenti [2010], Materassi et al [2011]. Materassi and Innocenti [2010] identify a tree-based structure, where in each node an additive noise disturbance is present, and only passive data is taken from the network.…”

Section: Introductionmentioning

confidence: 99%