Attitude Control of Micro/Mini Aerial Vehicles and Estimation of Aerodynamic Angles Formulated as Parametric Uncertainties

Mitikiri, Yujendra; Mohseni, Kamran

doi:10.1109/lra.2018.2807579

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…The existing work in the literature that shares the most similarities with this paper can be found in [58][59][60][61], where nonlinear attitude controllers for fixed-wing UAVs are devel-oped using quaternions, and that also use a model of the rotational dynamics. In [58,59], the translational and rotational subsystems are decoupled by estimating the higher-order derivatives of the angle of attack and sideslip angle.…”

Section: Related Workmentioning

confidence: 99%

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Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Coates

Fossen

2021

Applied Sciences

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This paper presents nonlinear, singularity-free autopilot designs for multivariable reduced-attitude control of fixed-wing aircraft. To control roll and pitch angles, we employ vector coordinates constrained to the unit two-sphere and that are independent of the yaw/heading angle. The angular velocity projected onto this vector is enforced to satisfy the coordinated-turn equation. We exploit model structure in the design and prove almost global asymptotic stability using Lyapunov-based tools. Slowly-varying aerodynamic disturbances are compensated for using adaptive backstepping. To emphasize the practical application of our result, we also establish the ultimate boundedness of the solutions under a simplified controller that only depends on rough estimates of the control-effectiveness matrix. The controller design can be used with state-of-the-art guidance systems for fixed-wing unmanned aerial vehicles (UAVs) and is implemented in the open-source autopilot ArduPilot for validation through realistic software-in-the-loop (SITL) simulations.

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Section: Related Workmentioning

confidence: 99%

“…This enables controllers for the two subsystems to be designed separately. In [60], a nonlinear PID controller for fixed-wing UAV (full) attitude control is presented. The control law is based on unit quaternions and compensates for aerodynamic coupling effects using integral action.…”

Section: Related Workmentioning

confidence: 99%

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Coates

Fossen

2021

Applied Sciences

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“…The last term in (6) represents the induced drag effect. The small α assumption is generally reasonable because the flight trajectory of a fixed-wing UAV usually does not include aggressive flights in which α is large [24]. Thus, assuming a small angle approximation for α in ( 6), the following linear relationship is obtained for the drag coefficient:…”

Section: A Synthetic Airspeed Estimationmentioning

confidence: 99%

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

Lim

Ryu

Rhudy

et al. 2022

IEEE Robot. Autom. Lett.

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A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-theart approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-theart methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

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“…3. This small α assumption is generally valid, as the flight trajectory of a fixed-wing UAV does not typically include aggressive flights with a high AOA α [21].…”

Section: Proposed Model-aided Synthetic Airspeed Estimationmentioning

confidence: 99%

Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

Youn

Ryu

Jang

et al. 2021

IEEE Robot. Autom. Lett.

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This paper proposes a novel method for modelaided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

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Attitude Control of Micro/Mini Aerial Vehicles and Estimation of Aerodynamic Angles Formulated as Parametric Uncertainties

Cited by 11 publications

References 9 publications

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Geometric Reduced-Attitude Control of Fixed-Wing UAVs

Deep Learning-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy With a Temporal Convolutional Network

Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

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