Seite - 221 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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A. Appendix
A.1. DerivationofExtendedKalmanFilter
When the input vectorV(k)is not zero, the state parameterAn(k−1)
is assumed to be constant likeAn(k−1) =Anct, whereAnct is the last
updated value ofAn. Then then-th MISO systems can be written as
suchas
Yn(k) =Anct ·Yn(k−1)+Ψn(k−1),
Ψn(k−1) =VT(k−1)[Φn(k−1)]V(k−1). (A.1)
Atanytimek, thevalueofΨn(k−1)canbecalculatedby
Ψn(k−1) =Yn(k)−Anct ·Yn(k−1), (A.2)
and then this value is used to estimate [Φn(k−1)] in equations
A.1.
In order to make the derivation process more intuitive to understand,
theoriginalvariablesV(k−1),Ψn(k−1)and [Φn(k−1)]arereplaced
bynewnotationsas
Γ(k) =V(k−1), ∆Yn(k) = Ψn(k−1), [Λn(k)] = [Φn(k−1)].
(A.3)
Thenthesecondequation in A.1canberewrittenas
∆Yn(k) =ΓT(k)[Λn(k)]Γ(k). (A.4)
AsinthelinearRKF(equation 3.59), inEKFit isalsoassumedthatthe
systemfulfills the followingequations
∆Ynr (k) =Γ
T(k)[Λn(k)]Γ(k)+ ς(k),
[Λn(k)] = [Λn(k−1)]+[ε(k−1)], (A.5)
221
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
- Kategorie
- Technik