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4. ControlSystemDesign
At time k
FuturePast
Prediction Horizon
kk-1 k+1 . . .
k+pk+2 Target output
Real output
Predicted output
Real control
Predicted control
Time
u(k)
Figure4.1. PrincipleofMPCcontrolalgorithms.
• Apply the first control action of the sequence as the real control
inputanddroptherest.
• Repeataboveproceduresateachtimeinstants.
The idea of MPC is illustrated by figure 4.1. Further elaborations of
MPCcanbefoundin[CBCB04]and[MHL99].
MPC has natural advantages over other control methods (mentioned
in thebeginningof thischapter) indealingwithcomplexsystems like
HEPHAISTOS:
• Theprinciple is intuitiveandeasy implement.
• The state-space model formed control law is suitable when the in-
putandoutputdimensionsare large.
• A great variety of dynamics including delay times and external
disturbancescanbeeasilyhandled.
• It involvespredictionsforthefuturebehaviorswhichareusefulfor
trackingtime-varyingtrajectories.
• It can easily be extended to treat different kinds of input and out-
putconstraints.
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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
- Kategorie
- Technik