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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.3. ResultsofDifferentControlMethods Through numerous practical heating experiments, the main conclu- sioncanbeobtainedthatthecontrollabilityofHEPHAISTOSislargely determined by the heating setup. To be more specific, different heat- ing setups create different boundary conditions for different parts of the workpiece, which significantly affects the overall controllabil- ity. For example, for the setup shown in figure 5.1 with large sizes (1.2m×1m×2mm), it is inevitable tohavehotspots inthecentralpart of the workpiece and cold spots in the corners and edges. A typical heatingprocesscanbe illustratedbythefigures in 5.17. (a) t=5min. (b) t=10min. (c) t=15min. (d) t=20min. Figure5.17. Heating process ( from (a) to (d) ) with the setup of aluminum plate. That is mainly due to different thermal conduction impacts from the aluminum plate and the metal table below the aluminum plate. Dur- 155
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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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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources