By Topic

Optimum control of an unknown linear plant using Bayesian estimation of the error

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

1 Author(s)
H. Spang ; General Electric Research Laboratory, Schenectady, NY, USA

In this paper, the theory of the optimum control of an unknown linear plant is discussed. Rather than try to estimate the coeffcients of the plant, the future error as defined by a quadratic measure is estimated using a Bayesian estimator. In this manner, better system performance can be expected since the effect of estimation errors on the estimator is obtained. The optimum control signal is then obtained which minimizes the estimated future error. It is shown that it is a linear function of the present output of the system. In the final section, a necessary and sufficient condition is obtained for the convergence of this procedure to the optimum system obtained with known coefficients.

Published in:

IEEE Transactions on Automatic Control  (Volume:10 ,  Issue: 1 )