Download Networked Embedded Sensing and Control: Workshop NESC’05: by Panos J. Antsaklis, Paulo Tabuada PDF

By Panos J. Antsaklis, Paulo Tabuada

This publication includes the lawsuits of the Workshop on Networked Embedded Sensing and regulate. This workshop goals at bringing jointly researchers engaged on assorted facets of networked embedded platforms so one can alternate examine reports and to spot the most clinical demanding situations during this fascinating new region.

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Read or Download Networked Embedded Sensing and Control: Workshop NESC’05: University of Notre Dame, USA, October 2005 Proceedings PDF

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Additional info for Networked Embedded Sensing and Control: Workshop NESC’05: University of Notre Dame, USA, October 2005 Proceedings

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J. Antsaklis, P. ): Netw. Emb. Sens. , LNCIS 331, pp. 25–51, 2006. © Springer-Verlag Berlin Heidelberg 2006 26 R. Carli et al. dination, the variables shared by the vehicles, converge to a common value, asymptotically. The problem of designing controllers that lead to such asymptotic coordination are called coordinated consensus problems, see for example [JLM03b, DD03, FM04a, OSM04a], and reference therein. The interest in these type of problems is not limited to the field of mobile vehicles coordination but also involves problems of synchronization [Str00a, MdV02, LFM05].

Let us consider the group ZN of integers modulo N and the Cayley graph G(ZN , S) where S = {−1, 0, 1}. Notice that in this case S is inverseclosed. Consider the uniform probability distribution π(0) = π(1) = π(−1) = 1/3 The corresponding Cayley stochastic matrix is given by   1/3 1/3 0 0 · · · 0 0 1/3 1/3 1/3 1/3 0 · · · 0 0 0 . P = .. .. . .. .  ..  . . . 1/3 0 0 0 · · · 0 1/3 1/3 Notice that in this case we have two symmetries. The first is that the graph is undirected and the second that the graph is circulant.

And all t ≥ 0, we have that, as in the previous case, the in order for condition (1) to hold and to have a non-negative matric, the feedback gains k0 , . . , kν must satisfy ν 1 + k0 , k1 , . . , kν ≥ 0 and k0 + i=1 ki = 0. The close loop system becomes ν ki Ei (t)x(t) . x(t + 1) = (1 + k0 )I + (12) i=1 Notice that also the system (12) can be regarded as Markov jump linear system. 3 Convergence and Performance Analysis In order to study the asymptotic behavior of the two previous strategy, it is convenient to introduce the variable y(t) that is defined in the following way.

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