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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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4.2. IntelligentControl of possible states forYmin is 4. The number of total position states is 5×4 =20. Based on the discretization schemes of temperatures and positions, the overall number of possible states for the modeled MDP should be 4×20 = 80. This number could be further reduced using the state abstractionstrategy[Die99]. Thecontrolobjective is toconstraintboth Ymax andYmin in the predefined temperature range. As long as they are in the range, the states of positions ofYmax andYmin become less important. Therefore all states withSmax = 1 andSmin = 1 can be combined into one state. Although this state abstraction makes state- actionvaluerelatedwiththisstatemorestochastic, thenumberoftotal state-action pairs can be largely reduced due to this abstraction. As a result, the number of total states after the state abstraction is |S| = 20×3+1 = 61. Besides the states, the number of total actions is also reduced based on the empirical knowledge. In the new HEPHAISTOS cavity 3, there are 18 microwave heating sources. Apparently there is no way to in- clude all of them in theQ(λ) learning controller (218 actions). Consid- ering the applicable scenario of this controller is the flat-temperature periodwherenohighpowerheatingisneeded,severalrulesaremade toscreenfor thesuitablecontrolactionsas following. 1. Each HEPHAISTOS module has 6 heating sources, but only 4 of themareused. Heatingsourcesno. 5andno. 8 (seefigure 2.5)are R1 R2 R4 R5 R3 Figure4.13. Areadiscretization. 129
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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