Real-Time Observability-Aware Inertia Parameter Estimation for Quadrotors

Kwon, Geonwoo; Lee, Jaegak; Yang, Hyunseok

doi:10.1109/access.2023.3254540

Cited by 3 publications

(8 citation statements)

References 37 publications

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“…In Kwon, (2023), the mass, the CoG position, and the principal inertial parameters of a simulated quadrotor are estimated using dedicated algorithms including recursive least squares (RLS) with and without a low pass filter. The needed angular acceleration values were obtained using a Kalman Filter.…”

Section: Related Workmentioning

confidence: 99%

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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: Related Workmentioning

confidence: 99%

“…Similar to Boyacioglu et al (2023) and Kwon, (2023), in Alabsi andTravis, (2019) the mass, the CoG position, and the inertia parameters were constant. Moreover, in Kwon, (2023) and Alabsi and Travis, (2019), the inertial parameters estimates were not utilized to update the involved flight controller.…”

Section: Related Workmentioning

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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“…These techniques primarily enable offline estimation of a quadrotor UAV's dynamic model based on pre-sampled datasets. Other time-domain methods, including recursive least squares (RLSs) [18], maximum likelihood [19], Bayesian filtering [15,[20][21][22], and sliding mode observer [23], are also widely utilized. The estimated parameters typically encompass mass, inertia matrix, and aerodynamic coefficients, all of which significantly influence control performance.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the inherent noise in sensor measurements on quadrotor UAVs, methods such as those described in [18,19] start by using a Kalman filter (KF) to preprocess the flight data before identification. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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Robust Geometric Control for a Quadrotor UAV with Extended Kalman Filter Estimation

Lei,

Liu,

Wang

2024

Actuators

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This study proposes a robust geometric controller tailored for quadrotor unmanned aerial vehicles (UAVs). The original geometric controller exhibits excellent performance in quadrotor UAV maneuvers. However, as a model-based nonlinear control method, it is sensitive to system model parameters. By integrating a novel extended Kalman filter (EKF)-based estimator for real-time, online estimation of the quadrotor’s inertia parameters, the controller adeptly handles internal uncertainties and external perturbations during flight maneuvers. This approach significantly improves the robustness of the control system against model inaccuracies. Empirical evidence is provided through both simulation and extensive real-world flight tests, demonstrating the controller’s effectiveness and its practical applicability in dynamic environments. The results confirm that this integration substantially enhances system reliability and performance under varied operational conditions.

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Data-Driven Hybrid Parametrically Optimized Adaptive Control Framework for Enhancing Stability Boundaries of Nonlinear Systems

Smith

Kamalasadan

2023

IEEE Trans. on Ind. Applicat.

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Real-Time Observability-Aware Inertia Parameter Estimation for Quadrotors

Cited by 3 publications

References 37 publications

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

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

Robust Geometric Control for a Quadrotor UAV with Extended Kalman Filter Estimation

Data-Driven Hybrid Parametrically Optimized Adaptive Control Framework for Enhancing Stability Boundaries of Nonlinear Systems

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