Konferensartikel

Nonlinear Observers based on the Functional Mockup Interface with Applications to Electric Vehicles

Jonathan Brembeck
German Aerospace Center (DLR) Oberpfaffenhofen, Insitute of Robotics and Mechatronics, Germany

Martin Otter
German Aerospace Center (DLR) Oberpfaffenhofen, Insitute of Robotics and Mechatronics, Germany

Dirk Zimmer
German Aerospace Center (DLR) Oberpfaffenhofen, Insitute of Robotics and Mechatronics, Germany

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp11063474

Ingår i: Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany

Linköping Electronic Conference Proceedings 63:53, s. 474-483

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Publicerad: 2011-06-30

ISBN: 978-91-7393-096-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

At DLR; an innovative electric vehicle is being developed that requires advanced; nonlinear control systems for proper functioning. Once central aspect is the use of nonlinear observers for several modules. A generic concept was developed and implemented in a prototype to automatically generate a nonlinear observer model in Modelica; given a contiuous (usually nonlinear) Modelica model of the physical system to be observed. The approach is based on the Functional Mockup Interface (FMI); by exporting the model in FMI format and importing it again in a form that enables the application of different observer designs; like EKF and UKF nonlinear Kalman Filters. The approach is demonstrated at hand of an observer for nonlinear battery model of the elctric vehicle of DLR.

Nyckelord

FMI; FMU; Kalman Filter; EKF; UKF

Referenser

[And04] André M. (2004): The ARTEMIS European driving cycles for measuring car pollutant emissions. Science of The Total Environment; 334-335; pp. 73-84. doi: 10.1016/j.scitotenv.2004.04.070.

[Bre11] Brembeck J.; Ho L. M.; Schaub A.; Satzger C.; Hirzinger P. G. (2011): ROMO – the robotic electric vehicle. IAVSD; Aug. 14-19; accepted for publication.

[Bre11b] Brembeck J.; Wielgos S. (2011): A real time capable battery model (mESC) for electro mobility applications using optimal estimation methods. Modelica’2011 Conference; March 20-22.

[Eng10] Engst C. (2010): Object-Oriented Modelling and Real-Time Simulation of an Electric Vehicle in Modelica. Master Thesis; Technische Universität München; Lehrstuhl für elektrische Antriebssysteme und Leistungselektronik. Supervisors: J. Brembeck; M. Otter; R. Kennel.

[FMI10] The Functional Mock-up Interface for Model Exchange;Version 1.0 (2010). ITEA2 MODELISAR Project. Download: www.functional-mockup-interface.org/fmi.html

[FMI11] Blochwitz T.; Otter M.; Arnold M.; Bausch C; Clauß C.; Elmqvist H.; Junghanns A.; Mauss J.; Monteiro M.; Neidhold T.; Neumerkel D.; Olsson H.; Peetz J.-V.; Wolf S. (2011): The Functional Mockup Interface for Tool independent Exchange of Simulation Models. Modelica’2011 Conference; March 20-22.

[Kan08] Kandepu R.; Imsland L.; Foss B. (2008): Constrained state estimation using the Unscented Kalman Filter. Proceedings of the 16th Mediterranean Conference on Control and Automation; pp. 1453-1458; June 25-27. doi: 10.1109/MED.2008.4602001.

[Lap99] Anderson E.; Bai Z.; Bischof C.; Blackford S.; Demmel J.; Dongarra J.; Du Croz J.; Greenbaum A.; Hammarling S.; McKenney A.; Sorensen D. (1999): Lapack Users’ Guide. Third Edition; SIAM. Download: http://www.netlib.org/lapack

[Lar98] Larsen T. D.; Andersen N. A.; Ravn O.; Poulsen N. K. (1998): Incorporation of time delayed measurements in a discrete-time Kalman filter. Proceedings of the 37th IEEE Conference on Decision and Control; vol. 4; pp. 3972-3977; Dec. 16-18.

[Lju98] Ljung; L. (1998): System Identification: Theory for the User. Prentice Hall; 2nd Edition.

[Mer01] Merwe R. V.; Wan E. (2001): The square-root unscented Kalman filter for state and parameter-estimation. Proceedings of the International Conference on Acoustics; Speech; and Signal Processingvol. 6; pp. 3461-3464.

[Mer04] Merwe R. V.; Wan E.; Julier S. (2004): Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. Proceedings of the AIAA Guidance Navigation & Control Conference; Providence; RI; Aug 2004. Download: http://www.csee.ogi.edu/~rudmerwe/pubs/pdf/vanderMerwe_GNC2004.pdf

[Mod10] Modelica Association (2010): Modelica – A Unified Object-Oriented Language for Physical Systems Modeling. Language Specification; Version 3.2. March 24; 2010. Download: https://www.modelica.org/documents/ModelicaSpec32.pdf.

[Phy10] Phython 3.1.2- Documentation (2010): Download: http://docs.python.org/py3k/

[Sim06] Simon D. (2006): Optimal State Estimation: Kalman; H Infinity; and Nonlinear Approaches. John Wiley & Sons Inc. doi: 10.1002/0470045345.

[Sim09] Simon D. (2010): Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory & Applications; vol. 4; no. 8; pp. 1303-1318; Aug. doi: 10.1049/iet-cta.2009.0032.

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