@inproceedings{444d14521507472f87e7592aeea71833,
title = "Kernel controllers: A systems-theoretic approach for data-driven modeling and control of spatiotemporally evolving processes",
abstract = "We consider the problem of modeling, estimating, and controlling the latent state of a spatiotemporally evolving continuous function using very few sensor measurements and actuator locations. Our solution to the problem consists of two parts: a predictive model of functional evolution, and feedback based estimator and controllers that can robustly recover the state of the model and drive it to a desired function. We show that layering a dynamical systems prior over temporal evolution of weights of a kernel model is a valid approach to spatiotemporal modeling that leads to systems theoretic, control-usable, predictive models. We provide sufficient conditions on the number of sensors and actuators required to guarantee observability and controllability. The approach is validated on a large real dataset, and in simulation for the control of spatiotemporally evolving function.",
keywords = "Dictionaries, High definition video, Hilbert space, Kernel, Mathematical model, Predictive models, Spatiotemporal phenomena",
author = "Kingravi, {Hassan A.} and Harshal Maske and Girish Chowdhary",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 54th IEEE Conference on Decision and Control, CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",
year = "2015",
month = feb,
day = "8",
doi = "10.1109/CDC.2015.7403382",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7365--7370",
booktitle = "54rd IEEE Conference on Decision and Control,CDC 2015",
address = "United States",
}