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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3.2. Grey-boxModeling Approximationsof themicrowaveheatingterm Compared with the heat loss part, the approximation of the mi- crowave heating term is more difficult, because of the complex mi- crowaveheatingpowersuperpositions. Ingeneral, therearetwotypes of microwave power superposition principles. The first power su- perposition principle corresponds to the situation when the EM fields from different sources are incoherent. In this case, the instantaneous microwave heating power of each source is varying, but the aver- age heating power is constant. Therefore the superposed heating power is equivalent to the addition of heating power of individual sources. For instance, assuming there are two microwave heating sources, the electric fields from two sources at the same locationn are −→ En,1 and−→ En,2, respectively. Theheatingpowerfromthetwosourcesarerepre- sentedby Pn,1mw∝ ∥∥−→En,1∥∥2, Pn,2mw∝∥∥−→En,2∥∥2. Thenthesuperposedheatingpower [BPJD99] is Pnmw=P n,1 mw+P n,2 mw∝ ∥∥−→En,1∥∥2+∥∥−→En,2∥∥2. (3.16) For HEPHAISTOS, considering multiple heating sources and control- lable feeding power, the total effective microwave heating power at the locationncanbewrittenas Pnmw= M∑ m=1 umP n,m mw , (3.17) whereum (1≤m≤M) stands for the portion of heating power from them-thfeedingsourceandPn,mmw isthemaximumheatingpowerfrom them-thsourcetothen-thlocation. Theaboveequation 3.17isrewrit- ten inamatrixmultiplicationformas Pnmw=P n mwU, (3.18) with U := [u1, u2, . . ., uM] T, Pnmw := [P n,1 mw, P n,2 mw, . . ., P n,M mw ], 47
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Title
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Author
Yiming Sun
Publisher
KIT Scientific Publishing
Location
Karlsruhe
Date
2016
Language
English
License
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Size
14.8 x 21.0 cm
Pages
260
Keywords
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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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources