0448768 20240111140908.820151019235959.9 84964331002 000369332000047 10.1109/CCA.2015.7320653 6 s. C cav_un_epca*0448758978-1-4799-7787-11085-1992347-352SydneyIEEE2015 nonlinear model predictive control building climate control cav_un_auth*0281469 Pčolka M. CZ cav_un_auth*0305702 Žáčeková E. CZ cav_un_auth*0304632 Robinett R. US cav_un_auth*0101074 Teorie řízení Department of Control Theory TŘ TR Department of Control Theory Čelikovský Sergej UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0021057 Šebek M. CZ článek 743 KB cav_un_auth*0292613 GA13-20433S GA ČR In this paper, the task of quantized nonlinear predictive control is addressed. In such case, values of some inputs can be from a continuous interval while for the others, it is required that the optimized values belong to a countable set of discrete values. Instead of very straightforward a posteriori quantization, an alternative algorithm is developed incorporating the quantization aspects directly into the optimization routine. The newly proposed quaNPC algorithm is tested on an example of building temperature control. The results for a broad range of number of quantization steps show that (unlike the naive a posteriori quantization) the quaNPC is able to maintain the control performance close to the performance of the original continuous-valued nonlinear predictive controller and at the same time it significantly decreases the undesirable oscillations of the discrete-valued input. cav_un_auth*0320745 IEEE Conference on Control Applications 2015 (CCA 2015) 21.09.2015-23.09.2015 Sydney AU BC 2016 5 PR RVO:67985556 http://hdl.handle.net/11104/0250397 cav_un_auth*0305697 Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Control Engineering ČVUT v Praze, FEL, Katedra řídicí techniky CZ S 2015 84964331002 SCOPUS 000369332000047 WOS článek 743 KB cav_un_epca*0448758 Proceedings of the 2015 IEEE Conference on Control Applications (CCA) 978-1-4799-7787-1 1085-1992 347 352 Sydney IEEE 2015