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4. ControlSystemDesign
neglected. Because they locate below the setup and their heating
effects are weaker than the other 4. In total 12 different heating
sourcesareused.
2. All control combinations consisting of more than 2 activated
sources are neglected, because 2 heating sources (maximum 4 kW
power) is enough to keep the workpiece of middle sizes (1m2 ∼
2m2) at the target temperaturerange(70◦C∼100◦C).
3. After the first two screenings, all left control combinations are fur-
ther tested and the combinations that only heat the center region
of theworkpieceareeliminated.
Followingtheabovethreerules,30differentcontrolactionsarefinally
selected to generate the action space. Combining the state and the ac-
tionspacestogether, thereareentirely1830(61×30)differentQvalues
to be estimated and saved by theQ(λ) learning controller. In theory,
even the control period is 1 s, it need at least 1830 s to go through the
overall state-actionspace. But inpractice, thehotspotsandcoldspots
arenotmovingthroughthewholeworkpieceduringoneheatingpro-
cess. It often happens that the states of the system stay in a limited
number of states and never go to other states, which means the num-
ber of practically possible state-action space is much smaller than the
theoretical value. In this case, the control task is realistic to be solved
inreal time(seetheresultsshowninchapter 4). Thecompletecontrol
procedures of the hybrid multi-agent control system are shown as in
figure 4.14.
130
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