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5.3. ResultsofDifferentControlMethods
0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0
02
0
4 0
6 0
8 0
1 0 0
1 2 0
DT2 = 2 . 8 ° C ~ 4 ° C
T i m e ( s ) T a r g e
tT
1T
2T
3T
4T
5
DT1= 4 . 5
°C
~5 °C
0
2 0
4 0
6 0
8 0
1 0 0
(b)LinearMPC(newCA3,12sources)
0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0
02
0
4 0
6 0
8 0
1 0 0
1 2 0
DT2= 3 . 5
°C
~3 . 8
°C
T i m e ( s ) T a r g e
tT
1T
2T
3T
4T
5
DT1= 3 . 3
°C
~3 . 7
°C
0
2 0
4 0
6 0
8 0
1 0 0
(c)NonlinearMPC(newCA3,8sources).
adjustedbytheself-coolingmatrixA. Butduetothesystemidentifica-
tion procedures introduced in chapter 3, the nonlinear MPC method
hasmuchlessopportunities toupdate thematrixA. Therefore itdoes
171
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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