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4. ControlSystemDesign
generalized in the continuous states and actions spaces by using
various functionapproximationmethods [SK00], respectively.
After its release in the 1980s, TD immediately became one of the most
popular RLC methods. Until now many improvements have been
done based on conventional TD methods (as introduced above). For
instance, that function approximation based approaches have been
proposed to extend the conventional critic-only methods into more
general methods which can deal with continuous states and actions,
such as the CMAC basedQ-learning [SSR97] or the wire-fiited NN
basedQ-learning [GWZ99]. More detailed introductions about TD
learningmethodsrefer to [Boy02]and[Si04].
4.2.2. DesignofReinforcementLearningController
Although TD methods have been widely implemented in various ap-
plications,mostofsystemsbeingcontrolledhavesimplearchitectures,
which means they have either discrete states and actions, or low in-
put and output space dimensions. When a TD learning controller is
used to deal with complex systems with both continuous variables
and high dimensions (such as HEPHAISTOS), the control system be-
comes much more complicated and in most cases the corresponding
learning process will become extremely slow. It is acceptable if the
controllercanbetrainedwithexperimentaldataepisodesintheoffline
form. However, in HEPHAISTOS it is impossible to generate training
data which can comprehensively cover all dynamics of the plant. In
this case, the TD learning control system has to be specially designed
andoptimized. Generally it isdevelopedandimplementedaccording
to the followingprinciples.
Hybridmulti-agentcontrolstructure
The hybrid multi-agent control structure is shown in figure 4.10. The
entire control system consists of two independent controllers that are
designed for different control objectives. One is a conventional adap-
tivecontroller thatcoulduseeitherMPCorNNC,andtheotheroneis
a lookup table based TD learning controller which will be introduced
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book Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources"
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