0478952 20240111140946.720171005235959.9 85032197261 000432014404022 10.23919/ChiCC.2017.8027897 6 s. E cav_un_epca*0478951978-1-5386-2918-53485-3490PiscatawayIEEE2017 Crank-Nicolson Discretization Method Iterative Learning Control Thin Plates cav_un_auth*0243458 Cichy B. PL cav_un_auth*0213204 Augusta Petr UTIA-B Teorie řízení Department of Control Theory TŘ TR Department of Control Theory Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0243459 Galkowski K. PL cav_un_auth*0228702 Rogers E. GB http://ieeexplore.ieee.org/document/8027897/ 2,26 MB An unconditionally stable finite difference discretization motivated aby the well-known Crank-Nicolson method is used to develop an iterative learning control design for systems whose dynamics are described by a fourh-order partial differential equation. A discrete in time and space model of a deformable rectangular mirror is derived in this setting and forms the basis for the new design, where a model for the actuator dynamics is also included. A numerical case study is given to demonstrate the properties of the new design. cav_un_auth*0350832 The 36th Chinese Control Conference 20170726 20170728 Dalian CN BC 10000 10200 10201 2018 4 PR RVO:67985556 http://hdl.handle.net/11104/0275246 cav_un_auth*0350833 Institute of Control and Computation Engineering, University of Zielona Góra PL cav_un_auth*0350834 Department of Electronics and Computer Science, University of Southampton GB S 8027897 RIV/67985556:_____/17:00478952!RIV18-AV0-67985556 191975702 Doplnění UT WOS 3+4 Proceedings Paper Automation Control Systems|Computer Science Artificial Intelligence|Engineering Electrical Electronic 3+4 Proceedings Paper Automation Control Systems|Computer Science Artificial Intelligence|Engineering Electrical Electronic 3+4 Proceedings Paper Automation Control Systems|Computer Science Artificial Intelligence|Engineering Electrical Electronic 2017 85032197261 SCOPUS PUBMED 000432014404022 WOS 2,26 MB cav_un_epca*0478951 Proceedings of the 36th Chinese Control Conference 978-1-5386-2918-5 3485 3490 Piscataway IEEE 2017