An $\omega$-Free Gyro-Free Accelerometer Pair Algorithm for 2D Trajectory Reconstruction

Liu, Xinyu; Zhou, Qingfeng; Cheng, Chi‐Tsun; Wu, Jianjian; Wang, Xindi; Fan, Lisheng

doi:10.1109/tvt.2020.3043050

Cited by 5 publications

(1 citation statement)

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“…Gyroscopes are one important example of this kind of sensors; however, their size is large once compared to accelerometers which are another kind of inertial sensors. Besides the size problem, gyroscopes tend to consume more power than accelerometers (Liu et al, 2020). Note that, however; there are Micro-Electro-Mechanical Systems (MEMS) gyroscope sensors with low weight, size, and cost in the market that can be used to directly measure the angular velocity but not the angular acceleration that is needed to facilitate the mass and inertia estimations.…”

Section: Introductionmentioning

confidence: 99%

In-flight estimation of quadrotor mass and inertia using all-accelerometer

Al-Rawashdeh

Elshafei

Ouakad

2023

Journal of Vibration and Control

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In this paper, an on-line closed-loop identification of mainly mass and inertia of an under-actuated aerial vehicle, namely a quadrotor acting as an aerial manipulator, is presented. Being treated as a rigid body, only one set of eighteen- or six tri-axial linear accelerometers is used to facilitate such estimation. Force and torque disturbances acting upon the vehicle during identification are also estimated which will refine the overall estimation quality of the unknown parameters. Namely, recursive linear and nonlinear least squares methods are used to obtain initial and refined estimations, respectively. Random disturbances are introduced to the closed-loop system to ensure enough excitation of the overall system. Issues related to identifiability, stability and performance are discussed. Since the proposed method depends on general kinematical and dynamical analyses of motion, it is claimed that the presented method will be also applicable to a wider range of, mainly, aerial vehicles.

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Section: Introductionmentioning

confidence: 99%