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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3.2. Grey-boxModeling Kn(k−1) = [Pnp(k−1)]Π(k−1)[σ2 +ΠT(k−1)[Pnp(k−1)]Π(k−1)]−1, θne(k−1) =θnp(k−1)+Kn(k−1) [ Ynr (k)−θnp(k−1)Π(k−1) ] , [Pne(k−1)] = [ 1−Kn(k−1)Π(k−1)][Pnp(k−1)]. (3.61) Thevectorsupdatedfromthepredictionpart (equations 3.60)can be directly used in the estimation part (equations 3.61). After the estimation, the estimated coefficient vector θne(k−1) can be em- ployed to predict future temperature valueYn(k+ 1) because of θnp(k) =θ n e(k−1). Both [Ω]andσ2 canbeeitherpredefinedbythe user or estimated online using other approaches [A˚JPJ08]. When the covariances of the noise signals are perfectly known, RKF is guaranteedtoprovidetheoptimalestimation[Rib04]. But inprac- tice, this condition is not always fulfilled. Normally the covari- ances [Ω] and σ2 are used as the tuning elements of RKF to ad- just the estimation properties. Detailed derivation process of RKF refers to [WVDM00]or [Rib04]. It should be noted that, ifλ is set to be 1 and the matrix [Ω] is set to be a zero matrix, the two results (equations 3.55 and 3.61) would be exactly the same. In other words, the RLS solution coincidis es with theRKFsolutionwhenthecoefficientvectorθn(k) isdeterministicand thecost functionJRLS(k) (equation 3.55) isnon-weighted. As mentioned in the beginning of this section, the solutions of RLS and RKF given by equations 3.55 and 3.61 are expressed in a MISO form. For a MIMO model that containsN different measured tem- peratures, the update process has to be repeated forN times per esti- mationperiod. Analternative to theMISO-formsystemidentification is to replace equation 3.48 by the MIMO equation 3.41, and directly applyRLSorRKFtotheMIMOARXmodel. Inthiscase,onlyoneup- date process is needed per estimation period. But the problem is that the state matrix [A(k−1)] resulted from the MIMO update approach is not diagonal, which is against equation 3.39. Correspondingly the estimation result of the MIMO-form system identification is signifi- cantly different from the result of the MISO-form. The performance 61
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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
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Technik
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources