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5. ExperimentalResults
0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0
00
5
1 0
1 5
2 0
T i m e ( s ) P I
DL
i n e a r M P
CN
o n l i n e a r M P C
(d)Standarddeviationwithrespect to the target temperature.
Figure5.30. Control performance of PID, linear MPC and nonlinear MPC in
the second setup. Due to slightly different duration in the three
experiments, the comparison of standard deviation lasts until
2760s.
not have the flexibility to deal with fast varying effects from the ther-
malconduction,andthiskindofinfluenceisfurthertransmittedtothe
estimation error of the heating effect. Using the setup 5.29, the cool-
ing effect of each workpiece is more stable than the first setup, and
correspondingly theaccuracyof thenonlinearsystemidentification is
higher than the case with the first setup. Consequently, the final con-
trolperformance is improved.
In figure 5.30c, when the target temperature is increasing from 75◦C
to100◦C, therearethreecontrolaccidentsoccurredconsecutively. Due
to these three accidents, the controlled temperatures fail to follow the
target temperature curve and the standard deviation becomes much
larger than the normal level (from 1600 s to 1800 s). But after that
period, the system estimator takes a short time to recover from the
accidents and the control behavior is back to normal quickly. It also
172
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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