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001			104993
003			KnowledgeUnlatched
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008			210129p20192020xx o u00| u ger d
037			_5BiblioBoard
245	0	0	_aModel Predictive Control mit MATLAB und Simulink _bModel Predictive Control with MATLAB and Simulink / _cRainer Dittmar.
020			_a9781839626388
024	8		_ahttps://doi.org/10.5772/intechopen.86001
029	1		_ahttps://library.biblioboard.com/ext/api/media/10774e7f-1212-4d59-8a24-dc5a7c252143/assets/thumbnail.jpg
040			_aScCtBLL _cScCtBLL
100	1		_aDittmar, Rainer _eauthor.
264		1	_bIntechOpen,
300			_a1 online resource (1 p.)
506	0		_aAccess copy available to the general public. _fUnrestricted _2star
520			_aModel Predictive Control (MPC) is used to solve challenging multivariable-constrained control problems. MPC systems are successfully applied in many different branches of industry. The MPC ToolboxTM of MATLAB®/Simulink® provides powerful tools for industrial MPC application, but also for education and research at technical universities. This book gives an overview of the basic ideas and advantages of the MPC concept. It shows how MPC systems can be designed, tuned, and simulated using the MPC Toolbox. Selected process engineering benchmark examples are used to demonstrate typical design approaches and help deepen the understanding of MPC technologies. The book is aimed at engineers in industry interested in the development and application of MPC systems, as well as students of different technical disciplines seeking an introduction into this field.
588	0		_aDescription based on print version record.
590			_aIntechOpen Engineering 2019 - 2021
650		7	_aComputers / Mathematical & Statistical Software _2bisacsh
650		0	_aComputers
655		0	_aElectronic books.
758			_iIs found in: _aKnowledge Unlatched _1https://openresearchlibrary.org/module/2774bc74-146a-484f-a7ba-ab1d6a09bbfb
856	4	0	_uhttps://openresearchlibrary.org/content/10774e7f-1212-4d59-8a24-dc5a7c252143 _zView this content on Open Research Library. _70
999			_c33036 _d33036