Noise Generation for Continuous System Simulation

Andreas Klöckner
German Aerospace Center (DLR), Institute of System Dynamics and Control, Weßling, Germany

Franciscus L. J. van der Linden
German Aerospace Center (DLR), Institute of System Dynamics and Control, Weßling, Germany

Dirk Zimmer
German Aerospace Center (DLR), Institute of System Dynamics and Control, Weßling, Germany

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp14096837

Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:87, s. 837-846

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Publicerad: 2014-03-10

ISBN: 978-91-7519-380-9

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


Adding random disturbances to Modelica models is necessary to represent stochastic fluctuations like sensor noise; air gusts and road irregularities. In this paper; we present a library to specify a pseudo random noise for continuous-time simulations. The random number generator; a probability density function and a frequency spectrum can be defined independently. A new random number generator is proposed to generate a continuous random signal; which is proven to be highly suitable for continuous models. The performance of the noise models is tested in two benchmarks using an academic as well as a realistic model both showing a remarkable increase in simulation speed.


Noise; Stochastic Models; Random Number Generator


[1] Crispin W Gardiner. Handbook of Stochastic Methods: for Physics, Chemistry and the Natural Sciences. Springer Berlin, 2nd edition, 1985.

[2] François Panneton, Pierre L’ecuyer, and Makoto Matsumoto. Improved long-period generators based on linear recurrences modulo 2. ACM Transactions on Mathematical Software (TOMS), 32(1):1–16, 2006.

[3] Marcus Baur, Martin Otter, and Bernhard Thiele. Modelica libraries for linear control systems. In Proceedings of 7th International Modelica Conference, Como, Italy, September, pages 20–22, 2009.

[4] Dassault Systèmes AB. Dymola User’s Manual – Volume 2, 2011.

[5] Ralf Korn, Elke Korn, and Gerald Kroisandt. Monte Carlo Methods and Models in Finance and Insurance. Financial Mathematic Series. Chapman & Hall / CRC Press, 2010. ISBN 978-1-4200-7618-9.

[6] George EP Box and Mervin E Muller. A note on the generation of random normal deviates. The Annals of Mathematical Statistics, 29(2):610–611, 1958.

[7] Franciscus L. J. van der Linden, Clemens Schlegel, Markus Christmann, Gergely Regula, Christopher Hill, Paulo Giangrande, Jean-Charles Maré, and Imanol Egaña. Implementation of a Modelica Library for Simulation of Electromechanical Actuators for Aircraft and Helicopters. In Proceedings of the 10th International Modelica Conference, 2014.

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