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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.3. ResultsofDifferentControlMethods trol interface regularly sends a pulse signal to the watchdog timer e 200 ms, to prevent it from elapsing. If any software or hardware ac- cidents happen, the LabVIEW control interface fails to the send this pulse signal. Then the watchdog timer will elapse and switch off the powersupplies. 9 6 0 9 6 5 9 7 0 9 7 5 9 8 0 9 8 5 9 9 0 9 9 5 1 0 0 06 0 8 0 T i m e ( s ) T a r g e tT 1T 2T 3T 4T 5 0 2 0 4 0 6 0 8 0 1 0 0 Figure5.26. Control ’accident’ in theheatingprocess. In practical applications, since LabVIEW is not a pure real-time soft- ware, the time cost in each LabVIEW loop is not fixed. In some cases the loop takes more than 200 ms, and then the watchdog timer will elapse and switch off the power supplies. What happens in the next is shown in figure 5.26. No matter what control input is sent to the HEPHAISTOScavity, there isnomicrowaveheatingpowerandhence the temperatures drop quickly. In such cases, the LabVIEW control interface and the MATLAB control program are not interrupted since there is no signal from the power supplies or the watchdog timer to report their status. They continuously update the estimated system model and calculate new control input, based on the incorrect power- temperaturepairs. This kind of failure is difficult to be eliminated from a hardware as- pect (the timeout value of the watchdog timer is fixed), and it is also hard to be detected in the control program. Normally when a single ’accident’ occurs, the system can recover from it quickly, owing to the 165
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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