By Keyou You, Nan Xiao, Lihua Xie
This monograph makes a speciality of characterizing the soundness and function results of putting limited-capacity conversation networks inside a keep an eye on loop. The textual content indicates how integration of the tips of keep an eye on and estimation with these of communique and knowledge concept can be utilized to supply very important insights bearing on numerous primary difficulties such as:
· minimal info cost for stabilization of linear structures over noisy channels;
· minimal community requirement for stabilization of linear platforms over fading channels; and
· balance of Kalman filtering with intermittent observations.
A primary hyperlink is published among the topological entropy of linear dynamical structures and the capacities of conversation channels. The layout of a logarithmic quantizer for the stabilization of linear structures less than numerous community environments is usually broadly mentioned and strategies to many difficulties of Kalman filtering with intermittent observations are demonstrated.
Analysis and layout of Networked keep an eye on Systems will curiosity keep watch over theorists and engineers operating with networked platforms and will 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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Additional resources for Analysis and Design of Networked Control Systems
Xie, S. Sun, W. Xiao, Quantized filtering of linear stochastic system. Trans. Inst. Meas. Control 33(6), 683–698 (2011) 100. B. Anderson, B. Moore, Optimal Filtering Systems Sciences Series (Prentice-Hall, Englewood Cliffs, 1979) 101. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, S. Sastry, Kalman filtering with intermittent observations. IEEE Trans. Autom. Control 49(9), 1453–1464 (2004) 28 2 Entropies and Capacities in Networked Control Systems 102. K. Plarre, F. Bullo, On Kalman filtering for detectable systems with intermittent observations.
4. 1 Problem Formulation Consider a discrete LTI system xk+1 = Axk + Bu k , © Springer-Verlag London 2015 K. 1) 39 40 4 Data Rate Theorem for Stabilization Over Erasure Channels Fig. 1 Network configuration Decoder Channel sk uk γk−1 Encoder System xk fk Controller where xk ∈ Rn is the measurable state for feedback and u k ∈ Rm is the control input. To make the problem interesting, A ∈ Rn×n is assumed to be unstable. The network configuration under consideration is described in Fig. 1, where the controller is collocated with the system and can access the state xk at time k.
To the contrary, if the data resolution is relatively high, the effect of resolution loss may be neglected. Thus, it is natural to expect that there may exist a critical data resolution (data rate) below which the controller is unable to stabilize an unstable system in an appropriate sense. This chapter studies this problem, and is concerned with the stabilization of linear systems over a noiseless channel, where the data exchanged between the plant and the controller contain only limited information.
Analysis and Design of Networked Control Systems by Keyou You, Nan Xiao, Lihua Xie