Modeling quantized fading channels as uncertainty: A quasi-Signal-to-Noise-Ratio approach

In this paper, we propose a new approach to model the network communication channel broadly used in networked control systems, which normally carries a quantizer subject to multiplicative random noise (fading effect). This new modeling approach is robust control oriented which allows factorization of the quantized fading channels (QFC) into a disturbance path, involving both quantization and fading impacts, and a nominal dedicated path. A quasi-Signal-to-Noise-Ratio (qSNR) is introduced to characterize the QFC, mimicking the traditional characterization of a disturbance uncertainty set in the norm bound. Through an example, it is shown that this new modeling approach turns the stabilizing control problem for networked systems with QFC into a normal small-gain design issue and offers a nice physical interpretation of the communication channel when its impact is considered on the performance of networked control systems. It is also shown that this new channel model is comprehensive, providing an unified expression of QFCs with either relative-error or multiplicative-error quantization.

[1]  I. Miller Probability, Random Variables, and Stochastic Processes , 1966 .

[2]  Robin J. Evans,et al.  Feedback Control Under Data Rate Constraints: An Overview , 2007, Proceedings of the IEEE.

[3]  Wei-Yong Yan,et al.  Stability robustness of networked control systems with respect to packet loss , 2007, Autom..

[4]  Zidong Wang,et al.  Quantized $H_{\infty }$ Control for Nonlinear Stochastic Time-Delay Systems With Missing Measurements , 2012, IEEE Transactions on Automatic Control.

[5]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[6]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..

[7]  Xiang Chen,et al.  Discrete-Time H∞ Gaussian Filter , 2008 .

[8]  Kemin Zhou,et al.  Mixed /spl Hscr//sub 2/ and /spl Hscr//sub /spl infin// performance objectives. I. Robust performance analysis , 1994 .

[9]  Xiang Chen,et al.  Output feedback stabilization for networked control systems with quantized fading actuating channels , 2013, 2013 American Control Conference.

[10]  Koji Tsumura,et al.  Tradeoffs between quantization and packet loss in networked control of linear systems , 2009, Autom..

[11]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[12]  Yilin Mo,et al.  Kalman Filtering with Intermittent Observations: Critical Value for Second Order System , 2011 .

[13]  Nicola Elia,et al.  Remote stabilization over fading channels , 2005, Syst. Control. Lett..

[14]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[15]  Mi-Ching Tsai,et al.  Robust and Optimal Control , 2014 .

[16]  Richard H. Middleton,et al.  Networked control design for linear systems , 2003, Autom..

[17]  Xiang Chen,et al.  Multiobjective \boldmathHt/Hf Control Design , 2001, SIAM J. Control. Optim..

[18]  Nan Xiao,et al.  Feedback Stabilization of Discrete-Time Networked Systems Over Fading Channels , 2012, IEEE Transactions on Automatic Control.

[19]  Bruno Sinopoli,et al.  Kalman filtering with intermittent observations , 2004, IEEE Transactions on Automatic Control.

[20]  Richard H. Middleton,et al.  Feedback stabilization over signal-to-noise ratio constrained channels , 2007, Proceedings of the 2004 American Control Conference.

[21]  Kemin Zhou,et al.  H/sub /spl infin// Gaussian filter on infinite time horizon , 2002 .

[22]  Li Qiu,et al.  Quantify the Unstable , 2010 .