LMI approach for robust predictive control using orthonormal basis functions
by Humberto X. Araújo; Rafael R. De Araújo; Gustavo H.C. Oliveira
International Journal of Modelling, Identification and Control (IJMIC), Vol. 24, No. 3, 2015

Abstract: This paper is focused on the problem of process control by using robust model predictive control (RMPC) and orthonormal basis functions (OBF). A relevant class of RMPC algorithms is the one characterised by the use of the LMI framework. Most of them assume a polytopic representation of the process uncertainties and require full-state feedback. On the other hand, several works describing the theory and applicability of OBF in identification and control fields can be found in the literature. The present paper proposes the use of OBF for modelling time-varying processes and for synthesis of RMPC algorithm. Using this proposed approach, the process model can be written in terms of new states, which are known or measurable. This represents an advantage in real applications where frequently the actual states are not known. Simulation results, one of them based on a debutaniser column model, illustrate the proposed methodology. The examples highlight the applicability of the method in the sense that a state feedback RMPC strategy is applied in a context where the actual states are unknown.

Online publication date: Thu, 22-Oct-2015

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