Modelling, Estimation and Control of Networked Complex by Arturo Buscarino, Luigi Fortuna, Mattia Frasca, Alessandro

By Arturo Buscarino, Luigi Fortuna, Mattia Frasca, Alessandro Rizzo (auth.), Alessandro Chiuso, Luigi Fortuna, Mattia Frasca, Alessandro Rizzo, Luca Schenato, Sandro Zampieri (eds.)

The paradigm of complexity is pervading either technology and engineering, resulting in the emergence of novel techniques orientated on the improvement of a systemic view of the phenomena lower than research; the definition of robust instruments for modelling, estimation, and regulate; and the cross-fertilization of alternative disciplines and ways. This ebook is dedicated to networked structures that are the most promising paradigms of complexity. it really is verified that complicated, dynamical networks are strong instruments to version, estimate, and keep watch over many attention-grabbing phenomena, like agent coordination, synchronization, social and economics occasions, networks of serious infrastructures, assets allocation, details processing, or keep watch over over conversation networks. furthermore, it truly is proven how the hot technological advances in instant verbal exchange and lowering in expense and measurement of digital units are selling the looks of enormous low-cost interconnected structures, every one with computational, sensing and cellular functions.

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Extra resources for Modelling, Estimation and Control of Networked Complex Systems

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5 Simulation Results: Topological Interaction Scheme Models based on moving particles which interact within a suitable radius have been often used to describe the behaviour of animal and human groups [16]. In a recent paper, Ballerini et al. [4] show that the interaction between birds in airborne flocks is governed by a topological distance rather than a metric one. In particular, through an accurate study based on image processing performed on bird flocks they show that each bird interacts on average with a fixed number of neighbours (six-seven), rather than with all neighbours within a fixed metric distance.

Physical Review E 50, 1642–1644 (1994) 26. : General approach for chaotic synchronization with applications to communication. Physical Review Letters 74, 5028–5031 (1995) 27. : Synchronization and desynchronization of complex dynamical networks: an engineering viewpoint. IEEE Transactions on Circuits and Systems I 50(7), 1381–1390 (2003) 28. : A time-varying complex dynamical network model and its controlled synchronization criteria. IEEE Transactions on Automatic Control 50(6), 841– 846 (2005) 29.

N. In this case we have αmax = 1 and it is trivial to show that f = 0 is QUAD(ε I, ω¯ ), with ε = ω¯ . 1, 34 P. De Lellis, M. di Bernardo, and F. 2 0 0 5 t Fig. 6). 5 0 0 5 t Fig. 9). synchronization is asymptotically achieved (see Fig. 1) with the various σi settling to constant values. 16) σ˙i j = α x j − xi , (i, j) ∈ E . 1. The simulation shown in Fig. 2 confirms the effectiveness of such strategy in achieving synchronization while guaranteeing that all coupling gains converge towards some constant values.

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