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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3.2. Grey-boxModeling adjusted according to the specific situation, which could take a long timeandrequireextraefforts. Althoughblack-boxmodelinghasgreat potential to achieve a good performance, it is normally considered as the last resort and only utilized when no alternative is available [Lju97]. In comparison with black-box modeling, the number of unknown pa- rameters and the amount of experimental data required in grey-box modeling are generally less. Moreover, the first principles used in grey-box modeling also give certain physical interpretations to the parameters being estimated, which double-check the estimation ac- curacy and help to understand the internal physical properties of the system. However, in many cases the first principles are still too com- plicated to be directly applied or not complete to cover all dynamics of theoriginal system. Inordertoidentifythemostsuitablemodelforthemicrowaveheating processandmakeHEPHAISTOSadaptive todifferentheatingscenar- ios, both grey-box and black-box modeling approaches are applied in thisdissertation. 3.2. Grey-boxModeling Therearemainlythreeprocedurestoapplythegrey-boxmodelingap- proach. Thefirst step is to identify thephysicalprinciples thatgovern theentireenergyexchangeprocess. Ifnecessary,certainsimplification and approximation have to be implemented to transfer the original principles intocontrollable forms. The last step is toorganizeanddis- cretize the resulted models, in order to make them suitable for the followingcontrollerdesign. 3.2.1. FirstPrinciples Foranyunitcellonthesurfaceof theheatedloadsuchas infigure 3.1 withsizeof l× l× l (e.g. l=1cm), thecompleteenergy(temperature) 41 changingequationcanbederivedaccordingtothelawofconservation of energy[LNS04], suchas indicatedbytheequation
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Titel
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Autor
Yiming Sun
Verlag
KIT Scientific Publishing
Ort
Karlsruhe
Datum
2016
Sprache
englisch
Lizenz
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Abmessungen
14.8 x 21.0 cm
Seiten
260
Schlagwörter
Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
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Technik
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources