Sensor Placement for Flapping Wing Model Using Stochastic Observability Gramians
Abstract
Systems in nature are stochastic as well as nonlinear. In traditional applications, engineered filters aim to minimize the stochastic effects caused by process and measurement noise. Conversely, a previous study showed that the process noise can reveal the observability of a system that was initially categorized as unobservable when deterministic tools were used. In this paper, we develop a stochastic framework to explore observability analysis and sensor placement. This framework allows for direct studies of the effects of stochasticity on optimal sensor placement and selection to improve filter error covariance. Numerical results are presented for sensor selection that optimizes stochastic empirical observability in a bioinspired setting.
Cite
@article{arxiv.2310.00127,
title = {Sensor Placement for Flapping Wing Model Using Stochastic Observability Gramians},
author = {Burak Boyacıoğlu and Mahnoush Babaei and Amanuel H. Mamo and Sarah Bergbreiter and Thomas L. Daniel and Kristi A. Morgansen},
journal= {arXiv preprint arXiv:2310.00127},
year = {2024}
}
Comments
12 pages, 5 figures, 2 tables, to be published in the proceedings of the 2024 American Control Conference