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3. ModelingMicrowaveHeating
where Kn(k− 1) is defined as the estimation gain vector at time k
with the dimension 1× (1 +M). Equation 3.54 indicates that the
newestimationθne(k−1)canbeobtainedusingtheformerestimation
θne(k−2) plus a correction term. This correction term is proportional
to the gain vectorKn(k−1), as well as the difference betweenYnr (k)
and Yne (k). A brief illustration of the linear system identification is
representedbyfigure 3.3.
· Measure the temperature Yn(k)
· Estimate the vector θn(k-1) (An(k-1), Bn(k-1))
· Update θne (k-1) using θ
n
e (k-2)
· Consider θne (k-1) as θ
n(k) and use it for prediction of
Yn(k+1)
k-1 k+1k Time
Ynr(k-1) Y n
r(k) Y n
r(k+1)
U(k-1)
Available: Yn(k), U(k-1), θne (k-2); Target: θ
n
e (k-1)
U(k)
θn(k-1) θn(k)
Figure3.3. Illustrationof the linearsystemidentificationprocess.
Two linear recursive system identification algorithms have been ana-
lyzedandtested inourexperiments.
58
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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