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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults rubbersetup, it isreasonabletobelievetheycanachieveasimilartem- peraturehomogeneityusingtherealCFRPprepregs. The target temperature curve defined in the experiments is shown by figure 5.22. Consideringthesafety issueandthereliabilityof thevac- uumbagging, themaximumtarget temperature isset tobe100◦Cand the temperature increasing rate is 8◦C/min. The target temperature curve is defined as shown in figure 5.22, which is similar with the target temperaturecurveusedinrealCFRPcuringprocess [PPW+02]. A typical control performance of PID is shown in figure 5.23. The control period used in the PID controller is ∆t= 1.5 s. In figure 5.23, the first temperature T1 is used as the controlled temperature. For the controlled temperature T1, it can be perfectly controlled by the PID controller and it follows the target temperature curve closely during the entire heating process. However, the problem for the PID con- troller is that all other temperatures are not controlled at all. All dif- ferent temperaturecurveshavethesamechangingtrendandthetem- perature differences between each other are almost constant during the flat-temperature period. Although the final temperature window ∆T1and∆T2 (temperaturedifferencebetweenthemaximumandthe minimum temperatures) in the end of both flat-temperature periods (a)Workpieceusedin theexperiment. + + + + + T1 T2 T3 T4 T5 (b)Measuredtemperatures. Figure5.21. Pictureof theworkpieceandmeasuredtemperatures. 160
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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