By Keyou You, Nan Xiao, Lihua Xie

ISBN-10: 1447166140

ISBN-13: 9781447166146

ISBN-10: 1447166159

ISBN-13: 9781447166153

This monograph specializes in characterizing the steadiness and function results of placing limited-capacity verbal exchange networks inside of a keep an eye on loop. The textual content indicates how integration of the information of keep an eye on and estimation with these of communique and data concept can be utilized to supply very important insights bearing on numerous primary difficulties such as:

· minimal facts price for stabilization of linear platforms over noisy channels;

· minimal community requirement for stabilization of linear platforms over fading channels; and

· balance of Kalman filtering with intermittent observations.

A basic hyperlink is printed among the topological entropy of linear dynamical platforms and the capacities of communique channels. The layout of a logarithmic quantizer for the stabilization of linear platforms lower than quite a few community environments is additionally broadly mentioned and ideas to many difficulties of Kalman filtering with intermittent observations are demonstrated.

Analysis and layout of Networked keep an eye on Systems will curiosity keep an eye on theorists and engineers operating with networked structures and should even be used as a source for graduate scholars with backgrounds in utilized arithmetic, communications or keep an eye on who're learning such systems.

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Extra info for Analysis and Design of Networked Control Systems

Sample text

To overcome this limitation, a more rigorous information-theoretic approach was adopted. It turns out that the system dynamics poses a fundamental limitation on the communication data rate required for stabilizing noise free linear systems. It is worthy noting that the derivation of data rate theorem bears a long history in the literature under various settings [4–9]. The pioneering work [5] interprets the quantized data as the information coder, and established the data rate theorem for a scalar system, which was further studied in [6] from an information flow point of view.

Murray, Probabilistic performance of state estimation across a lossy network. Automatica 44(12), 3046–3053 (2008) 114. N. Xiao, L. Xie, M. Fu, Kalman filtering over unreliable communication networks with bounded Markovian packet dropouts. Int. J. Robust Nonlinear Control 19(16), 1770–1786 (2009) 115. L. Shi, M. Epstein, R. Murray, Kalman filtering over a packet-dropping network: a probabilistic perspective. IEEE Trans. Autom. Control 55(3), 594–604 (2010) 116. S. Kar, B. Sinopoli, J. Moura, Kalman filtering with intermittent observations: weak convergence to a stationary distribution.

Giannakis, S. Roumeliotis, SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Trans. Signal Process. 54(12), 4782– 4795 (2006) 69. K. You, L. Xie, S. Sun, W. Xiao, Multiple-level quantized innovation Kalman filter, in Proceedings of 17th IFAC World Congress, pp. 1420–1425 (2008) 70. V. Borkar, S. Mitter, S. Tatikonda, Optimal sequential vector quantization of Markov sources. SIAM J. Control Optim. 40(1), 135–148 (2001) 71. J. Xiao, A. Ribeiro, Z.

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Analysis and Design of Networked Control Systems by Keyou You, Nan Xiao, Lihua Xie

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