By Topic

Intelligent model reference nonlinear friction compensation using neural networks and Lyapunov based adaptive control

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)
Vos, D.W. ; MIT, Cambridge, MA, USA ; Valavani, L. ; von Flotow, A.H.

Two approaches to eliminating nonlinear effects that would otherwise render the linear controllers designed for a plant ineffective are shown to work well experimentally. A neural network compensation scheme assumes no a priori knowledge as to the structure of the nonlinearity and, with suitable computational capability and sufficient training and data, allows `inversion' of the undesirable nonlinear effects. Since the network is learning both a structure as well as parameter values, the computational load is high. A further problem is the lack of stability guarantees for the weight update procedure and the distinct possibility of the network converging to local minima in the error backpropagation algorithm, although these phenomena did not appear to be problematic in experiment. A Lyapunov-based strategy offers fast parameter estimation with vastly reduced computation loads and hence the capability of adapting to varying surface friction conditions in real time

Published in:

Intelligent Control, 1991., Proceedings of the 1991 IEEE International Symposium on

Date of Conference:

13-15 Aug 1991