Unmanned Aerial Vehicle Speed Estimation via Nonlinear Adaptive Observers

Benzemrane, Khadidja; Santosuosso, Giovanni L.; Damm, Gilney

doi:10.1109/acc.2007.4282827

Cited by 25 publications

(19 citation statements)

References 10 publications

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“…The simulations are performed with the following parameters ( [9]) m = 2.5 kg, I xx = 224931 10 −7 kg.m 2 , I yy = 222611 10 −7 kg.m 2 and I zz = 325130 10 −7 kg.m 2 .…”

Section: Numerical Simulationmentioning

confidence: 99%

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Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

Rafaralahy

Richard

Boutayeb

et al. 2008

2008 47th IEEE Conference on Decision and Control

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In this contribution we investigate the problem of simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle (UAV). The main features lie in the use of a useful bank of reduced order observers to detect and isolate faulty sensors and in the same time to provide unbiased speed estimation of UAV from accelerometers. From a structural analysis, we provide all trajectories for which faults would be detected and estimated. The theoretical result is summarized into the stability analysis, established through the Lyapunov approach and Barbalat lemma. The existence condition is expressed in terms of physical variables of the UAV. Performances and easiness of implementation of the proposed technique are shown through a quadrotor UAV.

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“…The simulations are performed with the following parameters ( [9]) m = 2.5 kg, I xx = 224931 10 −7 kg.m 2 , I yy = 222611 10 −7 kg.m 2 and I zz = 325130 10 −7 kg.m 2 .…”

Section: Numerical Simulationmentioning

confidence: 99%

“…In [9], the authors propose a nice technique to estimate the speed of a quadrotor UAV from acceleration measurements, provided by Inertial Measurement Units. The approach is based on an adaptive observer technique using cascade…”

Section: Introductionmentioning

confidence: 99%

Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

Rafaralahy

Richard

Boutayeb

et al. 2008

2008 47th IEEE Conference on Decision and Control

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“…Recently, Bonnabel, Martin, and Rouchon (2006), Vik and Fossen (2001), Zhao and Slotine (2005), Benallegue et al (2008), Besnard et al (2007), Boutayeb et al (2008), Benzemrane et al (2007), Benzemrane (2009), Madani and Benallegue (2007) and Pflimlin et al (2007) have proposed observers to be implemented on UAVs. The critical issue in this domain comes from the fact that the translation velocity is classically known as non-observable.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear speed estimation of a GPS-free UAV

Santosuosso¹,

Benzemrane²,

Damm³

2011

International Journal of Control

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International audienceIn this article, the problem of robust state observer design for a class of unmanned aerial vehicles (UAVs) is addressed. A prototype four-rotors helicopter robot for indoors and outdoors applications is considered: the drone is not equipped with GPS related devices, so we describe a strategy to estimate its translational velocity vector based on acceleration, angles and angular speeds measurements only. Since the linearised system is non-observable at the equilibrium point, a nonlinear observability verification is performed for persistently exciting trajectories. A global exponential solution to this open problem is provided in the framework of adaptive observation theory when exact measurements are available. A modified observer is presented to enhance velocity estimation robustness in the realistic case of noisy measurements. The results are compared with a classical estimation strategy based on the extended Kalman filter to test the algorithm's performance

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“…Taking advantage of this observer, a feedback linearization controller [27] and a back-stepping controller [28] are employed to achieve MAV position tracking control in the presence of external disturbances. Nonlinear adaptive observer techniques are employed in [29] for velocity estimation of a quadrotor MAV, using noisy acceleration and angular measurements from IMUs, and the resulting observer demonstrates robustness to measurement noise in simulations. However, the proposed adaptive observer may be computationally demanding for onboard implementations since the cascade observer contains high-order terms.…”

Section: Related Workmentioning

confidence: 99%

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

Tang³

et al. 2015

Micromachines

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This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV's relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV's states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments. OPEN ACCESSMicromachines 2015, 6 488

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Unmanned Aerial Vehicle Speed Estimation via Nonlinear Adaptive Observers

Cited by 25 publications

References 10 publications

Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

Nonlinear speed estimation of a GPS-free UAV

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

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