Publication Date:
2022-05-25
Description:
© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Methods in Oceanography 10 (2015): 21–43, doi:10.1016/j.mio.2014.05.001.
Description:
We present an integrated framework for joint estimation and pursuit of dynamic features in the ocean, over large spatial scales and with multiple collaborating vehicles relying on limited communications. Our approach uses ocean model predictions to design closed-loop networked control at short time scales, and the primary innovation is to represent model uncertainty via a projection of ensemble forecasts into local linearized vehicle coordinates. Based on this projection, we identify a stochastic linear time-invariant model for estimation and control design. The methodology accurately decomposes spatial and temporal variations, exploits coupling between sites along the feature, and allows for advanced methods in communication-constrained control. Simulations with three example datasets successfully demonstrate the proof-of-concept.
Description:
The work is supported by the Office of Naval Research, Grant N00014-09-1-0700 and the National Science Foundation, Contract CNS-1212597.
Keywords:
Autonomous underwater vehicles
;
Collaborative control
;
Feature tracking
;
Ensemble forecasts
;
Linearization
;
System identification
Repository Name:
Woods Hole Open Access Server
Type:
Article
Format:
application/pdf
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