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4.2. IntelligentControl
DefinethestatespaceS andtheactionspaceU;
InitializeQ(s,u)arbitrarily foralls∈S,u∈U;
Initializee(s,u) = 0 foralls∈S,u∈U;
Initialize theadaptivecontrollerCad(Y,Ytar) (eitherMPCorNNC);
Take thefirst controlactionU(k)at timek= 1basedontheequation
U(k) =Cad(Y,Ytar)
Ateachtimek (k>1) :
repeat
1 Select theactivecontrollerCact(k)accordingto
Cact(k)⇐
Cad(Y,Ytar), when Ymax<=Ytar,
NoPower, when Ymin>=Ytar,
Q(s,u), otherwise.
ifCact(k) =Q(s,u) then
2 Calculate thecontrol actionU(k)andupdate theQvalues
accordingto theQ(λ) learningalgorithm(seefigure 4.12);
else
2 Calculate thecontrol actionU(k) =Cact(k);
3 Reset theeligibility tracematrixe(s,u) = 0 forall
s∈S,u∈U;
until the endof the controlprocess;
Figure4.14. Procedures in the hybrid multi-agentQ(λ) learning
control system.
131
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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
- Category
- Technik