Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter

Valthor Gudmundsson, Haukur Kristinsson, Soren Petersen, Agus Hasan

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.

OriginalsprogEngelsk
TitelModeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations : Modeling, Analysis, and Control
Antal sider10
Vol/bind3
ForlagAmerican Society of Mechanical Engineers
Publikationsdato2018
ISBN (Elektronisk)9780791851913
DOI
StatusUdgivet - 2018
BegivenhedASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, USA
Varighed: 30. sep. 20183. okt. 2018

Konference

KonferenceASME 2018 Dynamic Systems and Control Conference, DSCC 2018
LandUSA
ByAtlanta
Periode30/09/201803/10/2018
SponsorDynamic Systems and Control Division

Fingeraftryk

Unmanned aerial vehicles (UAV)
Kalman filters
Euler equations
Linear matrix inequalities
Fault detection
Dynamical systems

Citer dette

Gudmundsson, V., Kristinsson, H., Petersen, S., & Hasan, A. (2018). Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. I Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control (Bind 3). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2018-9123
Gudmundsson, Valthor ; Kristinsson, Haukur ; Petersen, Soren ; Hasan, Agus. / Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Bind 3 American Society of Mechanical Engineers, 2018.
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title = "Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter",
abstract = "This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.",
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Gudmundsson, V, Kristinsson, H, Petersen, S & Hasan, A 2018, Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. i Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. bind 3, American Society of Mechanical Engineers, ASME 2018 Dynamic Systems and Control Conference, DSCC 2018, Atlanta, USA, 30/09/2018. https://doi.org/10.1115/DSCC2018-9123

Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. / Gudmundsson, Valthor; Kristinsson, Haukur; Petersen, Soren; Hasan, Agus.

Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Bind 3 American Society of Mechanical Engineers, 2018.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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T1 - Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter

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AU - Petersen, Soren

AU - Hasan, Agus

PY - 2018

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N2 - This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.

AB - This paper presents a new approach for Unmanned Aerial Vehicle (UAV) attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. The nonlinear observer is globally asymptotically stable and is designed using linear matrix inequalities (LMI). The exogenous signal from the nonlinear observer is used to generate a linearized model for the Kalman filter. The method is implemented for attitude estimation of a quadcopter. The nonlinear model is derived from the Newton-Euler equations. The nonlinear model is locally Lipschitz due to the cross and dot products between the angular and linear velocity vectors. The attitude estimation from the dynamical system presented in this paper can be used as a module for fault detection. Simulations in Gazebo on a PX4 using Software In The Loop (SITL) shows the proposed method is able to estimate the attitude of a quadcopter accurately.

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BT - Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations

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Gudmundsson V, Kristinsson H, Petersen S, Hasan A. Robust UAV attitude estimation using a cascade of nonlinear observer and linearized Kalman filter. I Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations: Modeling, Analysis, and Control. Bind 3. American Society of Mechanical Engineers. 2018 https://doi.org/10.1115/DSCC2018-9123