Determining the UAV state space rotational dynamics model using algebraic inversion technique

Jemie Muliadi; Benyamin Kusumo Putro

doi:10.1145/3036331.3036355

Determining the UAV state space rotational dynamics model using algebraic inversion technique

Jemie Muliadi, Benyamin Kusumo Putro

Department of Electrical Engineering

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

1 Citation (Scopus)

Abstract

Adequate models of UAV's dynamics were important for a successful aerial mission. Such adequate model of flight dynamics were required to assemble a good flight controller. For modeling purposes, the state space methods have been applied in various system dynamics. In the conventional modeling, the UAV's state space constructed from the first principle which involved efforts of measurement and deals with uncertainties and errors in sensors reading. Hence, this work proposes a simplified method to identify the UAV state space directly from its flight data. The flight data were directly used to overcome the uncertainties issues. As the conclusion, this method were able to ommit the requirement for moments of inertia measurement compared to previous technique of State Space modeling.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Publisher	Association for Computing Machinery
Pages	52-56
Number of pages	5
ISBN (Electronic)	9781450348164
DOIs	https://doi.org/10.1145/3036331.3036355
Publication status	Published - 20 Jan 2017
Event	8th International Conference on Computer Modeling and Simulation, ICCMS 2017 - Canberra, Australia Duration: 20 Jan 2017 → 23 Jan 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128047

Conference

Conference	8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Country	Australia
City	Canberra
Period	20/01/17 → 23/01/17

Keywords

Linearized Newton-euler equation
Quadrotor modeling
State space identification
UAV

Access to Document

10.1145/3036331.3036355

Link to publication in Scopus

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Cite this

Muliadi, J., & Putro, B. K. (2017). Determining the UAV state space rotational dynamics model using algebraic inversion technique. In Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017 (pp. 52-56). (ACM International Conference Proceeding Series; Vol. Part F128047). Association for Computing Machinery. https://doi.org/10.1145/3036331.3036355

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abstract = "Adequate models of UAV's dynamics were important for a successful aerial mission. Such adequate model of flight dynamics were required to assemble a good flight controller. For modeling purposes, the state space methods have been applied in various system dynamics. In the conventional modeling, the UAV's state space constructed from the first principle which involved efforts of measurement and deals with uncertainties and errors in sensors reading. Hence, this work proposes a simplified method to identify the UAV state space directly from its flight data. The flight data were directly used to overcome the uncertainties issues. As the conclusion, this method were able to ommit the requirement for moments of inertia measurement compared to previous technique of State Space modeling.",

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Muliadi, J & Putro, BK 2017, Determining the UAV state space rotational dynamics model using algebraic inversion technique. in Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. ACM International Conference Proceeding Series, vol. Part F128047, Association for Computing Machinery, pp. 52-56, 8th International Conference on Computer Modeling and Simulation, ICCMS 2017, Canberra, Australia, 20/01/17. https://doi.org/10.1145/3036331.3036355

Determining the UAV state space rotational dynamics model using algebraic inversion technique. / Muliadi, Jemie; Putro, Benyamin Kusumo.

Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. Association for Computing Machinery, 2017. p. 52-56 (ACM International Conference Proceeding Series; Vol. Part F128047).

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

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