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Self-Optimizing and Adaptive Model Predictive Control of Electrical Drive Systems

Project acronym: DFG-SelfOpt-MPC
Project period: 3 years (2018/10/01 – 2021/09/30).
Project partner: German Research Foundation (DFG) under the reference number BO 2535/20-1

 Motivation

To fulfil process-technical tasks, electric drives are used in automated production lines, power plants, centrifuges and cranes as well as in road and rail vehicles. Usually, the drives are operated in closed-loop control, whereby in addition to the actual fulfilment of the control objective, secondary aspects such as loss-reduced or sensorless operation are increasingly desired or even demanded by the operators. The state of the art is represented by innovative approaches in singular fields of control methods for electrical drives. Among others, advanced methods for current and torque control [1], temperature estimation [2], parameter identification, optimized pulse patterns [3] or self-commissioning of electrical drives can be mentioned. Typically, these methods are only applied to individual problems for specific applications. In some cases, it was possible to combine several methods for one specific application, e.g. artificial neural networks with particle swarm optimization for temperature monitoring, in order to further increase the quality, efficiency and reliability of the results obtained so far [4]. A holistic control concept, which combines state-of-the-art methods as solutions for specific subproblems, has not yet been developed.

Project goals

A comprehensive generalized control concept for permanent magnet synchronous motors based on adaptive model predictive control with the following features shall be developed

  • Modelling of the control loop comprising controller, motor, inverter and load
  • Design and implementation of a current controller with online optimized adaptive pulse patterns for reduced motor und switching losses
  • Design and implementation of a superimposed self-commissioning torque controller
  • Development and integration of an online parameter identification method to adapt further control loops to improve controller performance
  • Development and integration of a temperature estimation algorithm for model predictive derating to prevent thermal overload or destruction of the electrical drive

The aim is to achieve high energetic efficiency of the electric drive through the features described above. In addition, the adaptive model-predictive approach is intended to maximize control flexibility. For this reason, the controller should be able to adapt itself optimally regardless of the application, e.g. traction drives in a road vehicle or spindle drives of a machine tool. All developed algorithms will be investigated by comprehensive simulative studies.  This involves a sophisticated motor, inverter and load model. In addition to that the methods will be validated by experimental studies on a test bench typical for automotive electrical drive applications (see Fig. 02).

References

[1]

M. Leuer, J. Böcker
„Self-Optimizing Model Predictive Direct Torque Control for ElectricalDrives“
24th International Symposium on Industrial Electronics (ISIE), IEEE, Buzios, Brazil,June 2015

[2]

O. Wallscheid, J. Böcker
„Design and Empirical Identification of a Lumped Thermal Parameter Network for Permanent Magnet Synchronous Motors with Physically Motivated Constraints”
International Electric Machines & Drives Conference (IEMDC), IEEE, Coeurd’Alene, Idaho (USA), May 2015

[3]

K. Peter, F. Mink, J. Böcker
„Model-Based Control Structure for High-Speed Permanent Magnet Synchronous Drives“
International Electric Machines & Drives Conference (IEMDC), IEEE, Miami, FL (USA), May 2017

[4]

O. Wallscheid, W. Kirchgässner, J. Böcker
„Investigation of Long Short-Term Memory Networks to Temperature Prediction for Permanent Magnet Synchronous Motors“
International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1940-1947, Anchorage, Alaska (USA), May 2017

 

Contact:

M.Sc. Anian Brosch

Kontakt
Vita
Publikationen
M.Sc. Anian Brosch

Leistungselektronik und Elektrische Antriebstechnik (LEA)

Wissenschaftlicher Mitarbeiter - Modellprädiktive direkte Drehmomentregelung permanent erregter Synchronmotoren

Telefon:
+49 5251 60-3011
Fax:
+49 5251 60-3443
Büro:
E4.133
Web:
Besucher:
Pohlweg 55
33098 Paderborn
M.Sc. Anian Brosch
01.10.2018 - heute

Wissenschaftlicher Mitarbeiter/ Doktorand

Modellprädiktive Regelungen im Kontext der Antriebstechnik

01.04.2018 - 30.09.2018

Entwicklungsingenieur

MdynamiX AG, An-Institut der Hochschule München

Entwicklung eines strombasierten Beobachters für Antriebssteuerungen.

01.10.2017 - 15.03.2018

Studentische Hilfskraft

Hochschule München (Labor für Regelungstechnik)

Entwicklungs eines modellprädiktiven Reglers für einen Lenkungsprüfstand.

01.03.2016 - 30.09.2017

Studentische Hilfskraft

Hochschule München (Labor für Akustik und Dynamik)

Entwicklung eines neuartigen Prüfstands zur akustischen Beurteilung von elektrischen Hilfsaggregaten in Fahrzeugen mit regelungstechnischer Auslegung.

01.03.2015 - 30.09.2015

Studentische Hilfskraft

Hochschule München (Labor für Fahrdynamik und Fahrzeugmechatronik)

Mitarbeit bei der Prüfstandsentwicklung: Programmierung von Mikrocontrollern.


Liste im Research Information System öffnen

2021

Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation

A. Brosch, O. Wallscheid, J. Boecker, IEEE Open Journal of Industry Applications (2021), pp. 1-1

DOI


Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors

A. Brosch, O. Wallscheid, J. Bocker, IEEE Transactions on Industrial Informatics (2021), pp. 1-1

DOI


Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments

G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021), pp. 187-201

DOI


2020

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors

A. Brosch, S. Hanke, O. Wallscheid, J. Bocker, IEEE Transactions on Power Electronics (2020), pp. 2179-2190

DOI


Liste im Research Information System öffnen

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