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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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4.2. IntelligentControl inRL. Inmostcases theplant tobecontrolled iscompletelyunknown tothelearner. Thereforethelearnerhastouseatrial-and-errorsearch- ing strategy to consistently explore the environment by performing different actions, and improve the action policy according to the re- ceivedrewardsfromtheenvironment. RLismorepracticalanduseful than supervised learning in many cases, because it is often impossi- ble to obtain training datasets that are both correct and representative to describe all dynamics of the real problem. That is also why RL is considered as the closest learning approach to functionalities of the humanbrain,andwidelyappliedandstudiedinartificial intelligence, roboticsandcomputerscienceareas [GBLB12]. RL was extended and implemented in the control field starting from the 1980s and 1990s [BSB81] [Sut84], and gradually developed as an influential control method [SBW92] [WD92] [WMS92]. The main dif- ferencebetweenRLcontrol (RLC)andothercontrolmethodsis that in RLC a different tool is utilized to describe the dynamics of the plant being controlled. Instead of transfer equations (polynomials) or state- spacemodelsusedinconventionalcontrolmethods, inRLtheplant is modeled by a Markov decision process (MDP) [Bel57] that only con- sistsofdifferentstate-actionpairsandtransitionprobabilitiesbetween any two states. The complete dynamics of the plant are described by theprobabilitydistribution Pa(s,s ′,r) =Pr(R(k+1) = r,S(k+1) =s′ |S(k) =s,A(k) =a), (4.31) wherePa(s,s′,r) is the transition probability from state s to state s′ witharewardr, causedbytheactionaat timek. Fromaconventionalcontrolengineeringpointofview,theactionA(k) isequivalent tothecontrol input(U(k))decidedbythecontroller,and the stateS(k) is the basis for making control actions, which functions similarly with the output variable (Y(k)). The rewardR(k) is the ba- sis for evaluating the control actions, to tell a control action is good or not,andithas thesamefunctionalitywiththecostdefinedbythecost function (such as equation 4.30). In order to keep a good consistency in matters of notations and descriptions, in the following the symbols U(k) and u are utilized to replaceA(k) anda, as the selected control action at time k and the random control action (vector), respectively. 111
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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
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources