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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5.3. ResultsofDifferentControlMethods 5.3.1. PerformanceofAdaptiveControlMethods In this part, four control methods have been tested and compared in the new HEPHAISTOS cavity 3 (new CA3, total 18 heating sources), including the PID control, the linear and the nonlinear MPC methods and the SPSA based NNC method. The PID control method is used as a benchmark for all other control methods. In the PID controller, only one temperature can be controlled and normally the maximum temperature is selected. The PID controller will calculate the control inputvalueaccordingto the followingequation[ACL05] u(k) =Kp ·e(k)+Ki · k∑ i=0 e(i)+Kd · e(k)−e(k−1) ∆t , e(k) =Yt(k)−Y(k), (5.2) where e(k) is the difference between the target temperature and the selectedtemperaturevalue. ThecoefficientKp,Ki,Kdare thepropor- tional gain, the integral gain and the derivative gain, respectively. All control input elementui(k) in the control vector U(k) take the same valueasu(k). Thesamesetupasshowninfigure 5.1isusedastheheatedworkpiece (0.5m×0.5m), and temperatures of five different locations are mea- sured by the infrared camera and controlled, such as in figure 5.21. The silicone rubber setup is used instead of real CFRP material in the experiments. In principle the control methods introduced in this dis- sertation can be applied to all different kinds of materials. For dif- ferent materials, the control performance would be different, and the final controlled temperature distribution mainly depends on the ther- mal conductivity of the setup or material. The thermal conductivity directly determines the accuracy of the estimated model and the ef- fective control diversity. From this point of view, the silicone rubber setup is a good experiment material, because it has a similar thermal conductivity with certain CFRP materials, e.g. the thermal conductiv- ity of silicone rubber is 0.2W/(m ·K)∼ 1.3W/(m ·K) [Sil12], and the thermalconductivityofatypicalCFRPepoxyprepregis0.6W/(m·K) [AD10]. If the control methods can perform well using the silicone 159
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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
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources