TY - GEN
T1 - Long-Horizon Direct Model Predictive Control Based on Neural Networks for Electrical Drives
AU - Hammoud, Issa
AU - Hentzelt, Sebastian
AU - Oehlschlaegel, Thimo
AU - Kennel, Ralph
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/18
Y1 - 2020/10/18
N2 - In this work, the use of a multilayer perceptron feedforward neural network is proposed to capture the solution of the long-horizon finite control set model predictive control (FCS-MPC) problem in electrical drive systems. The motivation behind this research is based on treating the direct model predictive control problem of a power converter as a multi-class classification problem as it consists of a finite set of switching states, which can be seen as a finite number of different classes. By simulation results and hardware in the loop (HIL) test, it is proved that the solution of the long-horizon FCS-MPC can be captured by a real-time computationally implementable neural network that recognizes the converter switching states with an accuracy of 85 - 90%. Hence, it captures the performance enhancement of long horizon FCS-MPC in a computationally efficient manner (15.84 μs).
AB - In this work, the use of a multilayer perceptron feedforward neural network is proposed to capture the solution of the long-horizon finite control set model predictive control (FCS-MPC) problem in electrical drive systems. The motivation behind this research is based on treating the direct model predictive control problem of a power converter as a multi-class classification problem as it consists of a finite set of switching states, which can be seen as a finite number of different classes. By simulation results and hardware in the loop (HIL) test, it is proved that the solution of the long-horizon FCS-MPC can be captured by a real-time computationally implementable neural network that recognizes the converter switching states with an accuracy of 85 - 90%. Hence, it captures the performance enhancement of long horizon FCS-MPC in a computationally efficient manner (15.84 μs).
KW - Long horizon direct model predictive control
KW - electrical drives
KW - finite control set model predictive control
KW - multi-class classification
KW - pattern recognition
KW - power converters
UR - http://www.scopus.com/inward/record.url?scp=85097771651&partnerID=8YFLogxK
U2 - 10.1109/IECON43393.2020.9254388
DO - 10.1109/IECON43393.2020.9254388
M3 - Conference contribution
AN - SCOPUS:85097771651
T3 - IECON Proceedings (Industrial Electronics Conference)
SP - 3057
EP - 3064
BT - Proceedings - IECON 2020
PB - IEEE Computer Society
T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Y2 - 19 October 2020 through 21 October 2020
ER -