Title :
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
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;
Conference_Titel :
Computational Intelligence in Control and Automation (CICA), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9902-1
DOI :
10.1109/CICA.2011.5945754
