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4. ControlSystemDesign
in equation 4.27 that both Y(+)(k) and Y(−)(k) are generated based
on Y(k), which means in the sequence (equation 4.28) the controller
shouldwaituntilY(+)(k)(Y(k+1))equivalent toY(k) to implement
the next control action U(−)(k). This condition requires extra wait-
ing time and also brings more disturbances for continuous multiple-
output systems, where two adjacent outputs are hardly identical. In
addition,theweightupdatefrequencyinthestandardSPSAalgorithm
is limited as one update per three control periods, which slows down
thewholecontrollerconvergingspeedandcorrespondinglydegrades
thecontrolperformance.
In order to make the SPSA algorithm more efficient in the practical
heating process, a semi-direct NN controller is implemented in this
dissertation. The first modification is to change the approximation of
thegradient fromequation 4.26 toasimpler formas[SC98]
h(w(k)) = J(+)(k)
c(k)∆(k) . (4.29)
Although the two-measurement strategy (equation 4.26) is gener-
ally more preferable, it has been proved in [Spa97] that this one-
measurement strategy (equation 4.29) is also suitable for highly non-
stationary systems, where the parameters of the plant or external dis-
turbancesmightchangeduringonecontrolperiod(fromk+1 tok+2
or fromJ(+)(k) toJ(−)(k)). In order to further speed up the learning
process, the control structure is also modified from the direct control
scheme to a semi-direct scheme that involves an additional NN esti-
mator (figure 4.6 ).
In this semi-direct scheme, the NN estimator keeps learning the dy-
namics of the real plant and monitoring the error between its predic-
tion and real output of the plant. If the prediction of the NN esti-
mator is constantly accurate for certain time (the MSE of prediction
is lower than a given threshold, described in figure 4.7), it will also
be used in the weight update. This NN estimator can be considered
as an approximation of the real plant, to provide information such as
Yˆ(+)(k), Jˆ(+)(k) to theNNcontroller. Duringeachcontrolperiod, the
NN controller can take one or more updates based on the NN estima-
tor,andthenumberofupdatespercontrolperiodcanbeadjusted. The
principlecanbefoundinthecontrollerdescription(figure 4.7).
106
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