Optimization of model predictive control by means of sequential parameter optimization

Author

Davtyan, A. ; Hoffmann, S. ; Scheuring, R.

Author_Institution

Inst. of Autom. & Ind. IT, Cologne Univ. of Appl. Sci., Cologne, Germany

fYear

2011

fDate

11-15 April 2011

Firstpage

11

Lastpage

16

Abstract

A methodology is developed for automatically tuning the main parameters of model predictive control (MPC) such as prediction horizon, control horizon and control interval. The tuning of parameters is done by means of sequential parameter optimization. In the process of optimization one of the major issues is the choice of an objective function. Several types of objective functions are tested in order to choose the one which solves the MPC tuning problem most adequate. In addition, different scenarios are analyzed if an exact model of the true plant does not exist.

Keywords

optimisation; predictive control; MPC tuning problem; model predictive control; objective function; sequential parameter optimization; Algorithm design and analysis; Mathematical model; Optimization; Prediction algorithms; Predictive control; Predictive models; Transfer functions; mean square error; model predictive control; objective function; sequential parameter optimization; transfer function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Control and Automation (CICA), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-9902-1

Type

conf

DOI

10.1109/CICA.2011.5945754

Filename

5945754