Estimation of maneuvering aircraft states and time-varying wind with turbulence

Lee, Je Hyeon; Sevil, Hakkı Erhan; Dogan, Atilla; Hullender, David A.

doi:10.1016/j.ast.2013.09.009

Cited by 28 publications

(6 citation statements)

References 14 publications

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“…The above model is obtained from the third row of the equation (1) of [11] by (a) including only longitudinal motions, and (b) equivalently replacing the terms of aerodynamic and propulsion forces R B T W T A T + P T in [11] with load factors [n x 0 n z ] T , as in the equation ( 14) of [5]. Note that measurements of true airspeed V t are unreliable in the presence of VCAS sensor fault [7], [8], thus cannot be directly used in the model (27).…”

Section: Appendix I Modeling Of Longitudinal Motionsmentioning

confidence: 99%

Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

Wan

Keviczky

Verhaegen

2016

2016 American Control Conference (ACC)

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This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using moving horizon estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated moving horizon estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the moving horizon estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general moving horizon estimation based residual generator. A highfidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.

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Section: Appendix I Modeling Of Longitudinal Motionsmentioning

confidence: 99%

Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

Wan

Keviczky

Verhaegen

2016

2016 American Control Conference (ACC)

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“…Besides, the ambient wind speed and direction are also very useful information for various objectives such as flight control [5], micro aerial vehicles [6], atmospheric energy harvesting [7][8][9], air traffic control [10], aerial refueling [11], and collision avoidance. A popular approach to the wind estimation problem involves applying the aircraft dynamic model to predict the information of wind through Kalman filter [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Estimation Method of Wind Speed and Direction Based on GNSS/INS/ADS Integrated Navigation

2021

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Arriving on time is of great importance for flight management and passenger experience. One of the essential factors that impacts on-time arrival is the wind condition. Accurate information of wind speed and direction around the fuselage helps to improve the performance of on-time arrival and four-dimensional trajectory (4DT) planning. To determine accurate wind information in real-time, a novel airborne estimation method of wind speed and direction is proposed in this paper. Inertial Navigation System (INS), Global Satellite Navigation System (GNSS), and Air Data System (ADS) are fused in an Unscented Kalman Filter (UKF), which provides great accuracy and robustness in nonlinearity conditions. The dynamic models of wind are established, and implementations of the UKF are detailed. Finally, simulations are designed and the effectiveness of the proposed method is verified through the comparison with the traditional direct measurement method. Results demonstrate that the accuracy of wind speed and direction obtained by our method is nearly two times higher than the traditional direct measurement method.

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“…This filter is called the velocity bias filter for wind estimation. In [7], the problem of aircraft wind velocity estimation is performed using the aircraft dynamic response rather than the wind triangle relationship. In this method, it is assumed that the mathematical model of the aircraft is perfectly known.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Kalman Filter for Fault Tolerant Estimation of Wind Speed and UAV Flight Parameters

Hajiyev

Cilden–Guler

Hacizade

2020

Measurement Science Review

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In this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is used with the help of the Global Positioning System (GPS) and dynamic pressure measurements. Extended Kalman Filter (EKF) is applied to the system. The state vector is composed of the wind speed components and the pitot scale factor. In the second stage, in order to estimate the state parameters of the UAV, GPS, and Inertial Measurement Unit (IMU) measurements are considered in a Linear Kalman filter. The second stage filter uses the first stage EKF estimates of the wind speed values. Between these two stages, a sensor fault detection algorithm is placed. The sensor fault detection algorithm is based on the first stage EKF innovation process. After detecting the fault on the sensor measurements, the state parameters of the UAV are estimated via robust Kalman filter (RKF) against sensor faults. The robust Kalman filter algorithm, which brings the fault tolerance feature to the filter, secures accurate estimation results in case of a faulty measurement without affecting the remaining good estimation characteristics. In simulations, noise increment and bias type of sensor faults are considered.

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Estimation of maneuvering aircraft states and time-varying wind with turbulence

Cited by 28 publications

References 14 publications

Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation

A Novel Estimation Method of Wind Speed and Direction Based on GNSS/INS/ADS Integrated Navigation

Two-Stage Kalman Filter for Fault Tolerant Estimation of Wind Speed and UAV Flight Parameters

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