By Topic

Optimal control of nonlinear systems using RBF neural network and adaptive extended Kalman filter

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

2 Author(s)
Medagam, P.V. ; Dept. of Electr. & Comput. Eng., Southern Illinois Univ. Carbondale, Carbondale, IL, USA ; Pourboghrat, F.

This paper presents a nonlinear optimal control technique based on approximating the solution to the Hamilton-Jacobi-Bellman (HJB) equation. The HJB solution (value function) is approximated as the output of a radial basis function neural network (RBFNN) with unknown parameters (weights, centers, and widths) whose inputs are the system's states. The problem of solving the HJB equation is therefore converted to estimating the parameters of the RBFNN. The RBFNN's parameters estimation is then recognized as an associated state estimation problem. An adaptive extended Kalman filter (AEKF) algorithm is developed for estimating the associated states (parameters) of the RBFNN. Numerical examples illustrate the merits of the proposed approach.

Published in:

American Control Conference, 2009. ACC '09.

Date of Conference:

10-12 June 2009