Seite - 182 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults
Despite the question whether theQ(λ) controller is fully trained dur-
ing the experiments, the results in figures 5.37 show its great poten-
tialandcapability tocontrol theentire temperaturedistributionof the
workpiece. Unliketheadaptivecontrolschemethat isbasedonthein-
dividual temperatures, theQ(λ)basedRLcontroller isable todirectly
controlthemaximumandtheminimumtemperatures. Theentiretem-
perature distribution is guaranteed to improve if both these two tem-
peratures are limited. From this point of view, the intelligent RLC
schemeismoreusefull forpractical industrialapplications.
182
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