By Topic

Statistics for sparse, high-dimensional, and nonparametric system identification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Anil Aswani ; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, 94720, USA ; Peter Bickel ; Claire Tomlin

Local linearization techniques are an important class of nonparametric system identification. Identifying local linearizations in practice involves solving a linear regression problem that is ill-posed. The problem can be ill-posed either if the dynamics of the system lie on a manifold of lower dimension than the ambient space or if there are not enough measurements of all the modes of the dynamics of the system. We describe a set of linear regression estimators that can handle data lying on a lower-dimension manifold. These estimators differ from previous estimators, because these estimators are able to improve estimator performance by exploiting the sparsity of the system - the existence of direct interconnections between only some of the states - and can work in the ldquolarge p, small nrdquo setting in which the number of states is comparable to the number of data points. We describe our system identification procedure, which consists of a pre smoothing step and a regression step, and then we apply this procedure to data taken from a quadrotor helicopter. We use this data set to compare our procedure with existing procedures.

Published in:

Robotics and Automation, 2009. ICRA '09. IEEE International Conference on

Date of Conference:

12-17 May 2009