Control of Systems through Quantized Channels with Multiplicative Random Noise
Xiang Chen
2013-12-19
Abstract: Networked control through quantized channels with multiplicative random noise is addressed in the signal-to
-noise-ratio (SNR) approach. In particular, a new modeling framework is proposed for the quantized channel with
multiplicative noise which allows a virtual function decomposition of the channel into a direct transmission path and
a disturbance path. The disturbance path is featured by the quantization error and the random noise and is characterized
in SNRs. Then the networked control design is presented by incorporating such a model. It is shown that, with two different
definitions of SNR, the control problem can be tackled in either robust stability or robust performance set-up. Under the
new approach, the profound relation among various existing results can be well illustrated and a systematic design framework
can be established for such kind of networked control problems. While most of existing results can be derived as special
cases from this new approach, it can also be applied to address multiple-input-multiple-output (MIMO) system with QFC in
both sensing and actuating channels which has not seen any published results so far. Therefore, it is concluded that the
new approach is comprehensive and generic.
About the Speaker:
Xiang Chen received his Ph. D. degree in system and control from Louisiana State University in 1998. Since 2000, he has held
cross-appointed positions in Department of Electrical and Computer Engineering and Department of Mechanical, Automotive and
Materials Engineering at the University of Windsor and is currently a Professor in the Department of Electrical and Computer
Engineering. He is currently an Associate Editor for SIAM Journal on Control and Optimization. He received Research Awards
(twice) from the University of Windsor. His research has been well supported by research funds from government agencies at
both federal and provincial levels in Canada and from industrial companies in both Canada and USA. His current research
interests include optimization and control (robust and optimal) of networked systems, graph-/game-theoretic approaches for
complex networked systems, as well as applications to automotive control systems and robotic vision sensor networks.