Accessibility navigation


Novel developments in process optimisation using predictive control

Becerra, V. M., Roberts, P.D. and Griffiths, G.W. (1998) Novel developments in process optimisation using predictive control. Journal of Process Control, 8 (2). pp. 117-138. ISSN 0959-1524

Full text not archived in this repository.

To link to this article DOI: 10.1016/S0959-1524(97)00046-2

Abstract/Summary

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:19195
Uncontrolled Keywords:optimal control, predictive control, process identification
Publisher:Elsevier

Centaur Editors: Update this record

Page navigation