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Design choices for the prediction and optimization stage of finite-set model based predictive control

Thomas Vyncke UGent, Steven Thielemans UGent, Tom Dierickx, Ruben Dewitte, Michiel Jacxsens UGent and Jan Melkebeek UGent (2011) Predictive Control of Electrical Drives and Power Electronics, Workshop proceedings. p.47-54
abstract
The interest in applying model-based predictive control (MBPC) for power-electronic converters has grown tremendously in the past years. This is due to the fact that MBPC allows fast and accurate control of multiple controlled variables for hybrid systems such as a power electronic converter and its load. As MBPC is a family of possible controllers rather than one single controller, several design choices are to be made when implementing MBPC. In this paper several conceptual possibilities are considered and compared for two important parts of online Finite-Set MBPC (FS-MBPC) algorithm: the cost function in the optimizations step and the prediction model in the prediction step. These possibilities are studied for two different applications of FS-MBPC for power electronics. The cost function is studied in the application of output current and capacitor voltage control of a 3-level flying-capacitor inverter. The aspect of the prediction model is studied for the stator flux and torque control of an induction machine with a 2-level inverter. The two different applications illustrate the versatility of FS-MBPC. In the study concerning the cost function firstly the comparison is made between quadratic and absolute value terms in the cost function. Comparable results are obtained, but a lower resource usage is obtained for the absolute value cost function. Secondly a capacitor voltage tracking control is compared to a control where the capacitor voltage may deviate without cost from the reference up to a certain voltage. The relaxed cost function results in better performance. For the prediction model both a classical, parametric machine model and a back propagation artificial neural network are applied. Both are shown to be capable of a good control quality, the neural network version is much more versatile but has a higher computational burden. However, the number of neurons in the hidden layer should be sufficiently high. All studied aspects were verified with experimental results and these validate the simulation results. Even more important is the fact that these experiments prove the feasibility of implementing online finite-set MBPC in an FPGA for both applications.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
MBPC, programmable digital hardware, FPGA implementation, predictive control, torque control, flying-capacitor inverters, induction motor
in
Predictive Control of Electrical Drives and Power Electronics, Workshop proceedings
pages
47 - 54
publisher
IEEE
place of publication
New York, NY, USA
conference name
Workshop on Predictive Control of Electrical Drives and Power Electronics (PRECEDE 2011)
conference location
Munich, Germany
conference start
2011-10-14
conference end
2011-10-15
Web of Science type
Conference Paper
Web of Science id
12377296
ISBN
9781457719127
DOI
10.1109/PRECEDE.2011.6078687
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2049680
handle
http://hdl.handle.net/1854/LU-2049680
date created
2012-02-28 15:57:13
date last changed
2012-03-29 10:25:11
@inproceedings{2049680,
  abstract     = {The interest in applying model-based predictive control (MBPC) for power-electronic converters has grown tremendously in the past years. This is due to the fact that MBPC  allows fast and accurate control of multiple controlled variables for hybrid systems such as a power electronic converter and its load. As MBPC is a family of possible controllers rather than one single controller, several design choices are to be made when implementing MBPC. In this paper several conceptual possibilities are considered and compared for two important parts of online Finite-Set MBPC (FS-MBPC) algorithm: the cost function in the optimizations step and the prediction model in the prediction step. These possibilities are studied for two different applications of FS-MBPC for power electronics. The cost function is studied in the application of output current and capacitor voltage control of a 3-level \unmatched{fb02}ying-capacitor inverter. The aspect of the prediction model is studied for the stator \unmatched{fb02}ux and torque control of an induction machine with a 2-level inverter. The two different applications illustrate the versatility of FS-MBPC. In the study concerning the cost function \unmatched{fb01}rstly the comparison is made between quadratic and absolute value terms in the cost function. Comparable results are obtained, but a lower resource usage is obtained for the absolute value cost function. Secondly a capacitor voltage tracking control is compared to a control where the capacitor voltage may deviate without cost from the reference up to a certain voltage. The relaxed cost function results in better performance. For the prediction model both a classical, parametric machine model and a back propagation arti\unmatched{fb01}cial neural network are applied. Both are shown to be capable of a good control quality, the neural network version is much more versatile but has a higher computational burden. However, the number of neurons in the hidden layer should be sufficiently high. All studied aspects were verified with experimental results and these validate the simulation results. Even more important is the fact that these experiments prove the feasibility of implementing online finite-set MBPC in an FPGA for both applications.},
  author       = {Vyncke, Thomas and Thielemans, Steven and Dierickx, Tom and Dewitte, Ruben  and Jacxsens, Michiel and Melkebeek, Jan},
  booktitle    = {Predictive Control of Electrical Drives and Power Electronics, Workshop proceedings},
  isbn         = {9781457719127},
  keyword      = {MBPC,programmable digital hardware,FPGA implementation,predictive control,torque control,\unmatched{fb02}ying-capacitor inverters,induction motor},
  language     = {eng},
  location     = {Munich, Germany},
  pages        = {47--54},
  publisher    = {IEEE},
  title        = {Design choices for the prediction and optimization stage of \unmatched{fb01}nite-set model based predictive control},
  url          = {http://dx.doi.org/10.1109/PRECEDE.2011.6078687},
  year         = {2011},
}

Chicago
Vyncke, Thomas, Steven Thielemans, Tom Dierickx, Ruben Dewitte, Michiel Jacxsens, and Jan Melkebeek. 2011. “Design Choices for the Prediction and Optimization Stage of Fnite-set Model Based Predictive Control.” In Predictive Control of Electrical Drives and Power Electronics, Workshop Proceedings, 47–54. New York, NY, USA: IEEE.
APA
Vyncke, T., Thielemans, S., Dierickx, T., Dewitte, R., Jacxsens, M., & Melkebeek, J. (2011). Design choices for the prediction and optimization stage of finite-set model based predictive control. Predictive Control of Electrical Drives and Power Electronics, Workshop proceedings (pp. 47–54). Presented at the Workshop on Predictive Control of Electrical Drives and Power Electronics (PRECEDE 2011), New York, NY, USA: IEEE.
Vancouver
1.
Vyncke T, Thielemans S, Dierickx T, Dewitte R, Jacxsens M, Melkebeek J. Design choices for the prediction and optimization stage of finite-set model based predictive control. Predictive Control of Electrical Drives and Power Electronics, Workshop proceedings. New York, NY, USA: IEEE; 2011. p. 47–54.
MLA
Vyncke, Thomas, Steven Thielemans, Tom Dierickx, et al. “Design Choices for the Prediction and Optimization Stage of Fnite-set Model Based Predictive Control.” Predictive Control of Electrical Drives and Power Electronics, Workshop Proceedings. New York, NY, USA: IEEE, 2011. 47–54. Print.