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5.3. ResultsofDifferentControlMethods
After the first trial, the preliminarily trainedQ(λ) is directly used in
the second trial, just as the case in the NNC experiments. The first
raising-temperature period of the second trial is controlled using the
nonlinear MPCmethod. In the flat-temperature period, the minimum
temperature isoscillatingaroundthetarget temperature, thereforeac-
cording to the principle defined in the hybrid control scheme (figure
4.14), the activated controller switching between the ’no power’ con-
trol and theQ(λ) controller. The temperature state mainly stays as
(Smax = 0,Smin = 1), which means the minimum temperature is in
the desired temperature range but the maximum temperature is not.
Although the locations states may vary during this oscillation period,
all state-action values related with (Smax = 0,Smin = 1) are tested for
multipletimesandupdatedtoahigheraccuracythantheothers. After
theoscillation, thestatesofthesystementersintothedesiredtempera-
turerangewith(Smax= 1,Smin= 1),whichmeansall temperaturesin
the controlled area are within the desired range and the control target
isachieved.
InordertofurthertesttheeffectivenessoftheQ(λ)controller, thethird
trial iscarriedoutconsecutively. Thestate-actionvaluestrainedinthe
second trial is loaded in the third trial. The raising-temperature pe-
riod of this trial is controlled by a NN controller. On the one hand,
the results in figure 5.37c are similar with the results in the second
trial. Both the maximum and the minimum temperatures stay in the
desired range for the most time of this trial, which means theQ(λ)
controller is effective to control the temperature distribution. On the
other hand, its effectiveness can not be fully confirmed by the results
in figure 5.37c. Because the temperature state in the third trial is the
same as the state in the second trial. In other words, after the update
process in the second trial, theQ(λ) controller may already have ac-
curate state-action values for all state-action pairs related to the state
(Smax = 0,Smin = 1). But it does not mean thisQ(λ) controller also
have the capability to deal with other temperature states. For exam-
ple, if the temperature state in the third trial is (Smax = 1,Smin = 0)
(themaximumtemperatureiswithintherangebuttheminimumtem-
perature is not), the controller may need a long time to update the
state-actionvaluesrelatedwith this stateagain.
181
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