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3. ModelingMicrowaveHeating
Thenthere
is∥∥∥−→En∥∥∥2=EnTEn
= Enx 2+Eny 2+Enz 2
= (
EnxV )T EnxV+ (
EnyV )T EnyV+ (
EnzV )T EnzV
=VTEnx TEnxV+V TEny TEnyV+V TEnz TEnzV
=VT (
Enx TEnx+E
n
y TEny+E n
z TEnz )
V (3.26)
The original microwave heating power Pnmw can be rewritten using
equation 3.26as
Pnmw= 1
2 l3σe(T) · ∥∥∥−→En∥∥∥2
= 1
2 l3σe(T) ·VT (
Enx TEnx+E n
y TEny+E
n
z TEnz )
V
=VT [Φnc(T)]V, (3.27)
where thematrix [Φnc(T)] isdefinedby
[Φnc(T)] := 1
2 l3σe(T) · (
Enx TEnx+E
n
y TEny+E
n
z TEnz )
. (3.28)
The matrix [Φnc(T)] is aM×M symmetric square matrix, which is
the effective heating matrix at this location n, and V is the control
input vector that has to be calculated by a controller. In contrast to
thecaseofscalarsuperposition, thevectorsuperpositionrule ismuch
more complicated due to its high computation complexity. However,
it is accurate and effective to describe the power superposition of mi-
crowave heating systems with multiple feeding sources, as shown by
thesimulationresults in [BPJD99]and[THN01].
3.2.3. FormulationandDiscretization
In this section, above approximations 3.15, 3.18 and 3.27 will be
further formulated and the resulted equations will be discretized, to
50
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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
- Category
- Technik