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4.1. AdaptiveControl
switches between only two states [MTA+06]. When the bang-bang
control scheme is used in the nonlinear model of HEPHAISTOS, it
is defined that the control input variables switch between 0 and 1,
which indicate the feeding source is turned off or turned on with full
power respectively. Compared with normal control where the control
variable can take any value between 0 and 1, it seems that the bang-
bangcontrolschemeslosesa largepartof theoverallcontroldiversity.
But when the dimension of control inputs is large enough such as in
HEPHAISTOS, the remained control diversity of such a binary con-
trol scheme is considerably large. For instance, in the new HEPHAIS-
TOS Cavity 3, the number of feeding sources is 18 (the old HEPHAIS-
TOS Cavity 3 had 36 sources), the total number of different heating
combinations is 218. It means in theory the system could provide 218
different heating patterns, which is still sufficient to provide a rather
uniformheating.
To derive the control solution in the bang-bang control schemes, a
binary programming algorithm [Sch98] has to be used. But due to
the special nonlinear form of 3.46, normal binary programming al-
gorithms are difficult to be implemented (most binary programming
algorithms can only deal with linear optimization problems [Sch98]).
Under this circumstance, the genetic algorithm(GA) is used here. GA
is a powerful global optimization and search algorithm inspired by
the natural evolution process [DAJ02]. Although many parts of GA
arestillnotwellunderstoodsuchas itsconvergingandstabilityprop-
erties, it can achieve surprisingly good performance for many prac-
tical problems where traditional algorithms could not work [HJK95].
Nowadays it has been one of the most successful optimization algo-
rithms that are widely used in computer science, engineering, eco-
nomicsandotherfields. Explicit introductionsaboutGAcanbefound
in[Whi94]. ThebinaryGAbasednonlinearMPCsystemofHEPHAIS-
TOScontains the followingfourprocedures.
• Step1: Initialization
At each time k, a number of possible control sequences are ran-
domlygenerated, suchas
Vci = [
Vi(k)
T Vi(k+1) T . .. Vi(k+p−1)T ]T
, 1≤ i≤Ng,
97
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