### Learning a dynamical system model for a spatiotemporal field using a mobile sensing robot

@inproceedings{lan_learning_2017,
title = {Learning a dynamical system model for a spatiotemporal field using a mobile sensing robot},
isbn = {978-1-5090-5992-8},
url = {http://ieeexplore.ieee.org/document/7962949/},
abstract = {This paper considers the problem of learning dynamic spatiotemporal ﬁelds using measurements from a single sensing robot. We ﬁrst introduce a widely used parametric dynamical model for the spatiotemporal ﬁeld. We then propose a motion strategy that can be used by a single sensing robot to collect sensor measurements about the ﬁeld. Our motion strategy is designed to collect sufﬁcient information at repeated locations but different times along an arbitrarily chosen periodic trajectory. In conjunction with the measurements collected, we propose a new learning algorithm based on subspace identiﬁcation to learn the parameters of the dynamical model. We prove that the parameters learned by our algorithm will converge to the true parameters as long as the periodic trajectory is uniformly observable and the number of measurements collected by the robot goes to inﬁnity. The performance of our algorithm is demonstrated in numerical simulations.},
language = {en},
urldate = {2020-09-15},
booktitle = {2017 {American} {Control} {Conference} ({ACC})},
publisher = {IEEE},
author = {Lan, Xiaodong and Schwager, Mac},
month = may,
year = {2017},
keywords = {learning\_dynamical\_systems},
pages = {170--175},
month_numeric = {5}
}