Real-Time Decision Making Seminar

An Information-Theoretic Approach to Control

We adopt a Shannon-theoretic view of remote stochastic linear control, showing coding theorems linking the amount of information passed through the feedback loop to several operational scenarios of interest. The controller aims to minimize a quadratic cost function in the state variables and control signal, known as the linear quadratic regulator (LQR), while communicating to the system via a rate-limited channel. For several channels of interest, namely, variable-length rate-limited noiseless channels, rate-limited packet drop channels, Gaussian channels, and biomolecular channels, we propose coding strategies that can approach these information-theoretic limits.
Based on joint works with B. Hassibi, A. Khina, A. Khisti, E.R. Gårding, G. M. Pettersson, Y. Nakahira, F. Xiao, J. C. Doyle.

Victoria Kostina joined Caltech as an Assistant Professor of Electrical Engineering in the fall of 2014. She holds a Bachelor's degree from Moscow institute of Physics and Technology (2004), where she was affiliated with the Institute for Information Transmission Problems of the Russian Academy of Sciences, a Master's degree from University of Ottawa (2006), and a PhD from Princeton University (2013). She is a recipient of the 2013 Princeton Electrical Engineering Best Dissertation Award, the 2015 Simons-Berkeley research fellowship, and the 2017 NSF CAREER award. Her research interests lie in information theory, theory of random processes, coding, wireless communications, and control.

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