0345615 20240111140741.520100830235959.9 6 s. www cav_un_epca*03456141-6Cape TownIFAC2010 parameter estimation process model identification steel industry cav_un_auth*0212695 Ettler P. CZ cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2010/AS/karny-parallel estimation respecting constraints of parametric models of cold rolling.pdf 1M0572 GA MŠk cav_un_auth*0001814 7D09008 GA MŠk CZ cav_un_auth*0261683 CEZ:AV0Z10750506 Model-based predictors and controllers frequently depend on efficient recursive estimation of model parameters. Similarly often, there are known hard bounds on parameter values. Adaptive control applied for rolling mills represents a typical example of such case. While common estimation algorithms are elaborated enough to be utilized in industrial practice, it is difficult to find implementation of bounded estimation, which is both formally consistent and suitable for reliable applications. Solution offered in this paper is based on simultaneous run of two or more proven estimators different in applied process models. Both simulated and real data examples are provided. cav_un_auth*0263644 Symposium on Automation in Mining, Mineral and Metal Processing /13./ Cape Town 02.08.2010-04.08.2010 ZA 2011 BB http://hdl.handle.net/11104/0186848 2010 pdf cav_un_epca*0345614 Proceedings 13th Symposium on Automation in Mining, Mineral and Metal Processing 1 6 Cape Town IFAC 2010