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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3. ModelingMicrowaveHeating thenequation 3.32 is further transferred into dYn dt = Anc(t) ·Yn+Bnc(t)U(t). (3.34) Basedonthissingleoutputmodel,acontinuous-timeMIMOmodel is builtas dY dt = [Ac(t)]Y+[Bc(t)]U(t), (3.35) whereY is theoutputvectordefinedas Y = [ Y1,Y2, .. . ,YN ]T , (3.36) andthestatematrixandtheoutputmatrixaredefinedas [Ac(t)] :=      A1c(t) 0 · ·· 0 0 A2c(t) · ·· 0 ... ... ... ... 0 0 · ·· ANc (t)      , [Bc(t)] :=       B1,1c (t) B 1,2 c (t) · ·· B1,Mc (t) B2,1c (t) B 2,2 c (t) · ·· B2,Mc (t) ... ... ... ... BN,1c (t) B N,2 c (t) · ·· BN,Mc (t)       . (3.37) Beforeabovemodelscanbeusedinrealcontrollerdesign, thelaststep is discretization, because in practice the controller is always operated indiscrete-time. Differentdiscretizationmethodsexistandinthisdis- sertationtheEulermethod[Lju98]isapplied. Whenthesamplingtime ∆t is a sufficiently small constant (1.5 s or even less), the temperature changing rate on the left-hand side of equation 3.35 can be approxi- matedby dY dt ≈ 1 ∆t ( Y(k+1)−Y(k) ) , wherek indicates thediscrete timeinterval. 52
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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