By Topic

Stochastic adaptive control and Martingale limit theory

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

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)
V. Solo ; Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA

Recently, S.P. Meyn and P.E. Caines (ibid., vol.AC-32, p.220-6, 1987) have used ergodic theory for Markov processes to give the first asymptotic stability analysis of a nontrivial stochastic adaptive control problem. By nontrivial is meant a stochastic adaptive control problem whose parameter variation has finite nonzero power. They correctly observed that the stochastic Lyapunov function methods fail here, because there is no almost sure parameter convergence. It is shown here how Martingale asymptotics can be used to produce many results close to those of Meyn and Caines, as well as to supply some new observations. Strengths and weaknesses of both approaches are discussed

Published in:

IEEE Transactions on Automatic Control  (Volume:35 ,  Issue: 1 )