By Topic

Learning augmented recursive estimation for uncertain nonlinear dynamical systems

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
$31 $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)
Draper, S.C. ; Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA ; Mangoubi, R.S. ; Baker, Walter L.

This paper describes a learning augmented recursive estimation approach for nonlinear dynamical systems having unmodeled nonlinearities. Utilizing a passive spatially-localized learning system, an approximation of the unknown nonlinearity is synthesized online, based on state and parameter estimates from a nonlinear recursive estimator (an adaptive form of the extended Kalman filter). The learned model of the nonlinearity is used, in turn, to improve the performance of the recursive estimator. We demonstrate the approach on a second-order, mass-spring-damper system, where the spring stiffness is a nonlinear function of position. Simulation results indicate that, relative to more traditional adaptive estimation schemes, markedly improved estimation performance can be achieved

Published in:

Intelligent Control, 1996., Proceedings of the 1996 IEEE International Symposium on

Date of Conference:

15-18 Sep 1996