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3.2. Grey-boxModeling
caused by different thermal radiation effects can be omitted, es-
pecially when the temperature is in a stable range. Then then-th
MISOsystemscanbewrittenas
Yn(k) =Anct ·Yn(k−1)+Ψn(k−1),
Ψn(k−1) =VT(k−1)[Φn(k−1)]V(k−1). (3.64)
Atanytimek, thevalueofΨn(k−1)canbecalculatedby
Ψn(k−1) =Yn(k)−Anct ·Yn(k−1), (3.65)
and then the effective heating matrix [Φn(k−1)] can be estimated
accordingto thevalueofΨn(k−1).
As inthelinearRKF(equation 3.59), inEKFit isalsoassumedthat
thesystemfulfills the followingequations
Ψnr(k−1) =VT(k−1)[Φn(k−1)]V(k−1)+ ς(k−1),
[Φn(k−1)] = [Φn(k−2)]+[ε(k−1)], (3.66)
whereΨnr(k−1) is therealcalculatedvalueofΨn(k−1), ς(k−1) is
azero-meanmeasurementorcalculationerrorwiththecovariance
σ2 and [ε(k−1)] is a zero-mean white noise with the covariance
matrix [Ω]. The definitions of parameters and vectors used in EKF
isshownbytable 3.2.
The detailed derivation process is enclosed in the appendix A.1.
TheupdateruleofEKFisgivenas following.
Predictionpartwithk≥2
:[
Φnp(k−1)
]
= [Φne(k−2)]
,[
Pnp(k−1)
]
= [Pne(k−2)]+[Ω]. (3.67)
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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