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5.3. ResultsofDifferentControlMethods
0 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0
04
0
6 0
8 0
1 0 0
T a r g e
tT
1T
2T
3T
4T
5
DT = 0 . 9 ° C ~ 1 . 1 ° C
DT = 1 3 . 5 ° C ~ 1 4 ° C
T i m e ( s )
Figure5.36. Simulationresultsof theQ(λ)basedRLCmethod.
0 5 0 0 1 0 0 0 1 5 0
02
0
4 0
6 0
8 0
1 0 0
DT = 1 2 . 8 ° C ~ 1 6 . 5 ° C
T i m e ( s ) T a r g e
tT
m a
xT
m i n
DT = 1 7 . 6 ° C ~ 1 8 ° C
0
2 0
4 0
6 0
8 0
1 0 0
(a) The first trial.
to 0.1 in the end of the heating process. In order to have a more stable
control performance, meanwhile also keep a certain exploration abil-
179
ity, the exploration probability used the in the last two trials is set to
be0.1.
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
- Kategorie
- Technik