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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
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
- Kategorie
- Technik