An Attitude Determination System For A Small Unmanned Helicopter Using Low-Cost Sensors

Gao, Tongyue; Zhang, Gong; Luo, Jun; Ding, Wei; Feng, Wei

doi:10.1109/robio.2006.340099

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…Sensors with such errors are commonly used in vertical take-off (VTOL) unmanned aerial vehicles (UAV) because of their low weight, small size, and low power consumption. By fusing the measurements of different sensors, an accurate estimate can be obtained [13][14][15][16][17].…”

Section: Figure 1 Maxi Joker 3 Flybarless Helicoptermentioning

confidence: 99%

Attitude and Flapping Angles Estimation for a Small-Scale Flybarless Helicopter Using a Kalman Filter

2015

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Unmanned aerial helicopters are essential for use in environments that are inaccessible for fixed wing aerial vehicles. Flybarless helicopters are famous for their high agility and maneuverability, which makes them suitable platforms in many challenging applications. This paper is concerned with the problem of estimating attitude and flapping angles of a flybarless, small-scale, single-rotor helicopter. The study utilizes a nonlinear model for the Maxi Joker 3 helicopter. A dynamic model-based Kalman filter is designed and implemented to estimate both the attitude and the flapping angles of the helicopter. Results of a simulation scenario are shown to validate the performance of the proposed approach. The results demonstrate high-accuracy flapping angles estimation with errors not exceeding,│Δ max │,0.3° in longitudinal flapping angles and 0.1° in lateral flapping angles. An experimental test is also conducted to demonstrate the performance of the method.

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Section: Figure 1 Maxi Joker 3 Flybarless Helicoptermentioning

confidence: 99%

Attitude and Flapping Angles Estimation for a Small-Scale Flybarless Helicopter Using a Kalman Filter

2015

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“…[5], [11]. However, it has been shown to achieve superior performance if the spacecraft is modeled as a rigid body where its states are described by two set of equations, "Euler equations of rotational dynamics" and "Kinematic equations" using MRP representation [11].…”

Section: A Nonlinear Plant Dynamicsmentioning

confidence: 99%

“…Typically, spacecraft attitude state vector is obtained by employing the kinematic equations -see e.g. [11], [4] and other references therein. In this paper, both kinematic and dynamics models are considered in order to compute the full state-vector of the spacecraft model.…”

Section: Introductionmentioning

confidence: 99%

New results on robust state estimation in spacecraft attitude control

Khan

Ahmad

2011

IEEE Conference on Decision and Control and European Control Conference

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In this paper, an integrated attitude estimation and control algorithm is addressed and implemented to a spacecraft dynamical model subject to observation (sensor) losses. Rigid body equations of motion for modeling and control of spacecraft model is obtained from both kinematic and dynamic equations. An earlier version of the so-called closed-loop estimation scheme presented in [9] is extended and implemented to the spacecraft model subject to observation losses. Compensated observation signals are reconstructed based on linear prediction subsystem and utilized at measurement update steps. Simulation results verify that the proposed robust estimation algorithm applied to the rigid body spacecraft model significantly outperforms existing open-loop filtering algorithms and could attack many other practical applications with intermittent output measurement losses.

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“…These sensors are commonly used in VTOL UAV because of their small weight, small size, and small power consumption. By fusing the measurements of different sensors, an accurate estimate can be obtained [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the strapdown method, which is based on integrating the angular rates to get the Euler angles, was introduced in [9,10]. Alternatively, the vehicle's attitude can be estimated using the bi-vector method.…”

Section: Introductionmentioning

confidence: 99%