Seite - 94 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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4. ControlSystemDesign
[Λ] :=
[Λ(1)] [OM] · ·· [OM]
[OM] [Λ(2)] · ·· [OM]
... ... ... ...
[OM] [OM] · ·· [Λ(p)]
,
andYd,t(k) is themodifiedtarget temperaturevectoras
Yd,t(k) = [
Yt(k)
T Yt(k+1) T . .. Yt(k+p−1)T ]T
.
Both [Γ] (Np×Np)and [Λ] (Mp×Mp)arepositivedefinitediagonal
matrices thatareusedtoadjustweightsof∆U(k)andYd,t(k) respec-
tively. Substituting the equations 4.12 and 4.13 into the above cost
function and differentiating both sides with respect to ∆Ud(k), the
analytical formedsolution isgivenas [Wan09]
∆Ud(k) = (
[Ξd(k)]
T
[Γ] T
[Γ] [Ξd(k)]+[Λ] )−1
[Ξd(k)]
T
[Γ] T
[Γ](
Yd,t(k)− [Fd(k)]Xd(k) )
,
U(k) = U(k−1)+∆U(k),
(4.16)
where∆U(k) is thefirstvector takenfrom∆Ud(k).
Equation 4.16 is the final control solution of the standard DMC algo-
rithm. For non constrained system, it can be directly implemented to
calculateU(k). However, for systems with control constraints, the re-
sulted control solutionU(k) does not automatically fulfill the desired
control constraints (such as equation 4.4). If the resulted U(k) does
not fulfill the input constraint, an additional quadratic programming
step is needed. Due to the modified system model 4.12, the form of
inputconstraintalsohas tobeadjustedas [Wan09]
Ud(k) = [Id] U(k−1)+[Πd] ∆Ud(k),
with
Ud(k) =
U(k)
U(k+1)
...
U(k+p−1)
, Id=
[IM]
[IM]
...
[IM]
,
94
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