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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

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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