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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults hand, the EKF based NN approach has a much smaller MSE than the nonlinear EKF algorithm in the validation part, which indicates the black-box approach is a more powerful tool to model systems with unknowndynamics. 5.3. ResultsofDifferentControlMethods The performance of different control methods is the most important result of this dissertation. In this section, the control performance of all aforementioned control methods is presented, with respect to dif- ferent setups. Before the experimental results of temperature control are demonstrated, an important question that has to be discussed is thecontrollabilityof thesystem. Controllability isan importantpropertyofasystem. Accordingto the definition in [Dor95], a system is controllable if any initial state of the system can be moved to any other state in a finite time interval us- ing the external input. Take HEPHAISTOS for example, it is control- lable if any desired temperature distributions can be obtained within a finite time interval using the control input from the initial tempera- ture distribution. Compared with this complete controllability, what ismoreimportant totheheatingapplications is thereachabilityofcer- tain states in HEPHAISTOS. A particular state of the system is reach- able if any initial states can be transfered to this state within a finite time intervals using a corresponding control input sequence [Rug96]. In HEPHAISTOS, the control task is to achieve a homogeneous tem- perature distribution for the whole workpiece, therefore the reacha- bilityof thehomogeneoustemperaturedistributionbecomesthemost importantpropertyof thesystem. Although it has been proved in [WYT12] that any arbitrary tempera- turedistributioncouldbeachievedbyacorrespondingEMfielddistri- bution in microwave heating, it neglected that not all EM field distri- butions can be realized within a microwave cavity. Moreover, the EM fielddistributionofHEPHAISTOSissofarnotpossibletobeobtained in neither analytical nor numerical ways. Due to these reasons, it is more realistic to analyze the controllability quantitively via a number of realexperiments. 154
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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