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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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4. ControlSystemDesign control isdefinednotonlybycharacterizingappliedcontrolmethods, but also by characterizing the control task being optimized. In other words, the control task used in the intelligent control system is also ”intelligent”. The so-called intelligent control task is named in com- parison with traditional control tasks. In conventional heating prob- lems, the mean square error (MSE) between measured temperatures and the target temperature is used as the cost (objective) function of thecontrol task,suchasequation 4.2usedintheMPCmethod. In many cases, the expression 4.2 is not always the best choice for the cost function. On the one hand, the true temperature distribution is not well represented by the value of the cost function. For example, during the heating process, there are a number of hot spots and cold spots that are not measured, and thus not reflected by the value of the cost function (equation 4.2). On the other hand, in applications where the entire temperature profile is available, only a small part of theprofileisusedinthecontrolof thecost function 4.2. Themostpart of the informationfromthethermalpicture iswasted. Initializeweightsof theNNcontrollerandtheNNestimator aswc andwe; Initialize functionsof theNNcontrollerandtheNNestimator asGandF; Set theestimationerror indicatorχindandits lower thresholdχth; Set theparametersc(k),α(k), thecounternumberCt= 1andthe indirect trainingtimernt= 1; Take thefirst controlactionasU(k) =Fc(Y(k),Ytar(k))at timek= 1; Figure4.7. Procedures in thesemi-directNNcontrol system(part1). In this case, it is better to replace the traditional cost function by more innovative and reliable definitions, which are the intelligent control tasks as previously mentioned. For example, using a infrared camera to measure the temperature profile of the heated load, the maximum and the minimum temperatures of the whole heated loadYmax,Ymin can be obtained in real time, and a more reliable cost function can be 108
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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