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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
- 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
- Category
- Technik