0448759 20240111140908.820151019235959.9 84964391182 000369332000021 10.1109/CCA.2015.7320627 6 s. C cav_un_epca*0448758978-1-4799-7787-11085-1992163-168SydneyIEEE2015 model predictive control identification cav_un_auth*0305702 Žáčeková E. CZ cav_un_auth*0281469 Pčolka M. CZ cav_un_auth*0021057 Šebek M. CZ 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. článek 356 KB This paper addresses the problem of model predictive control for a class of nonlinear systems which satisfies persistent excitation condition. The conditions under which a nonlinear system description can be handled are specified and two algorithms (one optimizing the first input sample and the other considering optimization of an M-sample subsequence of the input profile) solving the persistent excitation condition within a predictive controller for nonlinear systems are developed, both maximizing the smallest eigenvalue of the information matrix increase. The numerical experiments performed on a test-bed system demonstrate that the algorithms are able to successfully improve identifiability of a nonlinear system description while keeping the original controller performance degradation lower than arbitrarily chosen level cav_un_auth*0320745 IEEE Conference on Control Applications 2015 (CCA 2015) 21.09.2015-23.09.2015 Sydney AU BC 2016 4 PR RVO:67985556 http://hdl.handle.net/11104/0250398 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 84964391182 SCOPUS 000369332000021 WOS článek 356 KB cav_un_epca*0448758 Proceedings of the 2015 IEEE Conference on Control Applications (CCA) 978-1-4799-7787-1 1085-1992 163 168 Sydney IEEE 2015