System identification for an unmanned aerial vehicle using the maximum likelihood method

Shanshan Yu; Zhengjie Wang; Christoph Gottlicher

doi:10.1109/ICMIC.2017.8321524

System identification for an unmanned aerial vehicle using the maximum likelihood method

Shanshan Yu, Zhengjie Wang, Christoph Gottlicher^*

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

System identification provides one way of efficiently obtaining dynamic models. This paper applies maximum likelihood method for system identification to determine numerical values of model parameters based on measured system outputs. In the paper at hand, the rigid body equations of motion are modeled using the software FALCON.m, which is able to automatically compute the model's analytic derivatives. The validity of the approach is demonstrated by applying it to simulated data. Comparing the estimation results with the original data shows very good agreement, thus proving the applicability of the method in this context.

Original language	English
Title of host publication	Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	589-594
Number of pages	6
ISBN (Electronic)	9781509065738
DOIs	https://doi.org/10.1109/ICMIC.2017.8321524
Publication status	Published - 2 Jul 2017
Event	9th International Conference on Modelling, Identification and Control, ICMIC 2017 - Kunming, China Duration: 10 Jul 2017 → 12 Jul 2017

Publication series

Name	Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017
Volume	2018-March

Conference

Conference	9th International Conference on Modelling, Identification and Control, ICMIC 2017
Country/Territory	China
City	Kunming
Period	10/07/17 → 12/07/17

Keywords

Maximum Likelihood Method
System Identification
Unmanned Aerial Vehicle

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10.1109/ICMIC.2017.8321524

Cite this

Yu, S., Wang, Z., & Gottlicher, C. (2017). System identification for an unmanned aerial vehicle using the maximum likelihood method. In Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017 (pp. 589-594). (Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017; Vol. 2018-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMIC.2017.8321524

Yu, Shanshan ; Wang, Zhengjie ; Gottlicher, Christoph. / System identification for an unmanned aerial vehicle using the maximum likelihood method. Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 589-594 (Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017).

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abstract = "System identification provides one way of efficiently obtaining dynamic models. This paper applies maximum likelihood method for system identification to determine numerical values of model parameters based on measured system outputs. In the paper at hand, the rigid body equations of motion are modeled using the software FALCON.m, which is able to automatically compute the model's analytic derivatives. The validity of the approach is demonstrated by applying it to simulated data. Comparing the estimation results with the original data shows very good agreement, thus proving the applicability of the method in this context.",

keywords = "Maximum Likelihood Method, System Identification, Unmanned Aerial Vehicle",

author = "Shanshan Yu and Zhengjie Wang and Christoph Gottlicher",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th International Conference on Modelling, Identification and Control, ICMIC 2017 ; Conference date: 10-07-2017 Through 12-07-2017",

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doi = "10.1109/ICMIC.2017.8321524",

language = "English",

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Yu, S, Wang, Z & Gottlicher, C 2017, System identification for an unmanned aerial vehicle using the maximum likelihood method. in Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017. Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017, vol. 2018-March, Institute of Electrical and Electronics Engineers Inc., pp. 589-594, 9th International Conference on Modelling, Identification and Control, ICMIC 2017, Kunming, China, 10/07/17. https://doi.org/10.1109/ICMIC.2017.8321524

System identification for an unmanned aerial vehicle using the maximum likelihood method. / Yu, Shanshan; Wang, Zhengjie; Gottlicher, Christoph.
Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 589-594 (Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017; Vol. 2018-March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gottlicher, Christoph

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N2 - System identification provides one way of efficiently obtaining dynamic models. This paper applies maximum likelihood method for system identification to determine numerical values of model parameters based on measured system outputs. In the paper at hand, the rigid body equations of motion are modeled using the software FALCON.m, which is able to automatically compute the model's analytic derivatives. The validity of the approach is demonstrated by applying it to simulated data. Comparing the estimation results with the original data shows very good agreement, thus proving the applicability of the method in this context.

AB - System identification provides one way of efficiently obtaining dynamic models. This paper applies maximum likelihood method for system identification to determine numerical values of model parameters based on measured system outputs. In the paper at hand, the rigid body equations of motion are modeled using the software FALCON.m, which is able to automatically compute the model's analytic derivatives. The validity of the approach is demonstrated by applying it to simulated data. Comparing the estimation results with the original data shows very good agreement, thus proving the applicability of the method in this context.

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BT - Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017

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Yu S, Wang Z, Gottlicher C. System identification for an unmanned aerial vehicle using the maximum likelihood method. In Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 589-594. (Proceedings of 2017 9th International Conference On Modelling, Identification and Control, ICMIC 2017). doi: 10.1109/ICMIC.2017.8321524

System identification for an unmanned aerial vehicle using the maximum likelihood method

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