0361358 20240103195354.120110816235959.9 6 s. cav_un_epca*0361357978-989-8425-74-4284-289PortugalskoSciTePress – Science and Technology Publications2011 Bayesian modelling Hierarchical model Parameter estimation cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0212695 Ettler P. CZ http://library.utia.cas.cz/separaty/2011/AS/dedecius-adaptive continuous hierarchical model-based decision making.pdf 7D09008 GA MŠk CZ cav_un_auth*0261683 1M0572 GA MŠk cav_un_auth*0001814 CEZ:AV0Z10750506 Industrial model-based control often relies on parametric models. However, for certain operational conditions either the precise underlying physical model is not available or the lack of relevant or reliable data prevents its use. A popular approach is to employ the black box or grey box models, releasing the theoretical rigor. This leads to several candidate models being at disposal, from which the (often subjectively) prominent one is selected. However, in the presence of model uncertainty, we propose to benefit from a subset of credible models. The idea behind the multimodelling approach is closely related to hierarchical modelling methodology. By using several modelling levels, it is possible to achieve relatively high quality and robust solution, providing a way around typical constraints in industrial applications. cav_un_auth*0273291 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO) Noordwijkerhout 27.07.2011-31.07.2011 NL 2012 BB 2 http://hdl.handle.net/11104/0198685 2011 cav_un_epca*0361357 Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics 978-989-8425-74-4 284 289 Portugalsko SciTePress – Science and Technology Publications 2011