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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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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
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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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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources