Generalised predictive control for non-linear process systems based on lazy learning
by Tianhong Pan, Shaoyuan Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 1, No. 3, 2006

Abstract: This paper presents an approach to model and control a discrete-time non-linear dynamical system on the basis of a finite amount of input/output observations. In order to improve the accuracy of local modelling based on lazy learning, a similarity criterion together with distance measure and angle measure is advanced. When the system works in the steady state or varies slowly, an adaptive tuning algorithm is used to alleviate the modelling computational burden. Also, this model is cast into a Generalised Predictive Control (GPC) framework. As a result of the integrated methodology, the cost, time and effort required to develop a non-linear generalised predictive control strategy is greatly reduced. The advantages of the methodology are demonstrated on non-linear function (a non-minimum phase system) and Continuous Stirred Tank Reactor (CSTR) simulations.

Online publication date: Wed, 03-Jan-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com