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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.3. ResultsofDifferentControlMethods ods. Compared with the PID controller, NNC is also much better be- causeof thebetter temperaturehomogeneity. 0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 02 0 4 0 6 0 8 0 1 0 0 1 2 0 DT2 = 5 ° C ~ 5 . 5 ° C T i m e ( s ) T a r g e tT 1T 2T 3T 4T 5 DT1 = 5 ° C ~ 5 . 5 ° C 0 2 0 4 0 6 0 8 0 1 0 0 Figure5.33. ControlperformanceofNNC(newCA3,8sources). When the weights of the NN controller are properly initialized, this temperature lagphenomenoncanbesignificantlyreduced. Forexam- ple, the control results of another NNC experiment is shown in figure 5.34. In this experiment, the controller is initialized with the same weights as the trained controller in 5.33. In other words, after the ex- perimentshowninfigure 5.33, thesamecontroller isusedagaininthe sameenvironment todealwith thesamecontrol task. From the results in figure 5.34, a big improvement can be observed is that thecontrolledtemperatures followthetarget temperatureclosely almost from the beginning of the control process. There is no tem- perature overshoot and the temperature window is smaller than the resultsshowninfigure 5.33. Thisperformanceimprovementisdueto the appropriate initialization of the NN controller. A good initializa- tion would speed up the entire controller update and lead to a more reliablecontrolperformance in thebeginningperiod. From the control results shown by these pictures, all three adaptive control methods, including the linear MPC, the nonlinear MPC and 175
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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