A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs

Sankaralingam, L.; Ramprasadh, C.

doi:10.1016/j.cja.2019.11.003

Cited by 32 publications

(12 citation statements)

References 42 publications

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“…The airflow angles, i.e., the angle of attack 𝛼 and the sideslip angle 𝛽, are commonly measured by mechanical vanes, while the true airspeed 𝑉 TAS can be measured by a Pitot-static probe. It is also possible to measure the air triplets by a multi-hole Pitot probe [15]. According to Eqs.…”

Section: Aircraft Groundmentioning

confidence: 99%

Wind and Airflow Angle Estimation Using an Adaptive Extended Rauch-Tung-Striebel Smoother

Fang

Visser

Pool

et al. 2022

AIAA SCITECH 2022 Forum

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This paper proposes a new method that estimates the three-dimensional stochastic wind velocity for an aircraft equipped with a Pitot-static tube and airflow vanes. Since the performance of most state estimators, e.g., the extended Rauch-Tung-Striebel smoother, relies on the process and measurement noise covariance settings, the proposed method employs the expectationmaximization approach to estimate the noise covariance matrices to improve the estimation accuracy. Numerical simulations demonstrated that the proposed method can successfully estimate the noise covariance matrices, especially for the noise covariance of the wind velocity, using the measurement data and reconstruct the wind velocity offline. Additionally, the smoothed true airspeed, angle of attack, and angle of sideslip data are more accurate compared to the direct measurements. This feature is also beneficial for other applications such as the aerodynamic model identifications of aircraft.

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Section: Aircraft Groundmentioning

confidence: 99%

Wind and Airflow Angle Estimation Using an Adaptive Extended Rauch-Tung-Striebel Smoother

Fang

Visser

Pool

et al. 2022

AIAA SCITECH 2022 Forum

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“…Concerning estimation of AoA using intelligent control systems, there have been many studies, and the studies go on this matter (Sankaralingam and Ramprasadh, 2019;Cheng and Dasheng, 2019;Ramprasadh et al, 2018;Ly, 2017). Petritoli et al (2021) developed a fuzzy model to estimate AoA.…”

Section: Fault Detection and Diagnosis Of Maneuvering Characteristics Augmentation Systemmentioning

confidence: 99%

The requirements for automation systems based on Boeing 737 MAX crashes

Demirci

2021

AEAT

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Purpose This paper aims to show the current situation and additional requirements for the aircraft automation systems based on the lessons learned from the two 737 MAX crashes. Design/methodology/approach In this study, the Swiss cheese model was used to find the real root causes of the 737 MAX accidents. Then, the results have been compared with the actions taken by the manufacturers and authorities. Based on the comparison, the necessary improvements to prevent such accidents are defined. Regarding the faulty sensor that forms the accidents, a synthetic sensor was developed using an aerodynamic model. Findings It has been proven that the safety-critical automation systems should not be designed by relying on a single set of sensor data. Automation levels should be defined in a standard way. Depending on the defined automation level, the system must be designed as either fail-safe or fail-operational system. When designing backup systems, it should be decided by looking at not only whether it has power but also the accuracy of the incoming signals. Practical implications Aviation certification requirements related to automation systems need to be revised and improved. With this context, it was revealed that the certification processes for automation systems should be re-evaluated and updated by aviation authorities, especially Federal Aviation Administration and European Union Aviation Safety Agency. Originality/value Task sharing between automation system and pilot based on the classification of automation levels and determining certification requirements accordingly has been brought to the agenda. A synthetic Angle of Attack sensor was developed by using an aerodynamic model for fault detection and diagnosis.

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“…Estimates of these parameters can be used for fault detection and changes due to structural damage or adverse flight conditions that could alter the aerodynamic coefficients of a UAV, and could also allow precise control of small UAVs in particular maneuvers, such as vertical landing [19] or high dynamic flight [20]. A comprehensive review of some methodologies and estimation approaches for typical fixed-wing and rotary-wing UAVs can be found in [21], in which the authors point out that multihole probes based on pressure measurements are difficult to calibrate for low-speed, low-altitude flights; optical methods are unreliable for small UAVs; and potentiometer-based vane probes could be suitable for applications in mini flyers. In the literature, attempts at estimation without direct measurements of AOA and AOS in small UAVs, using algebraic methods or multistage fusion algorithms with linear time-varying models, can also be found [22,23].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Airspeed, Angle of Attack, and Sideslip for Small Unmanned Aerial Vehicles (UAVs) Using a Micro-Pitot Tube

Ariante¹,

Ponte²,

Papa³

et al. 2021

Electronics

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Fixed and rotary-wing unmanned aircraft systems (UASs), originally developed for military purposes, have widely spread in scientific, civilian, commercial, and recreational applications. Among the most interesting and challenging aspects of small UAS technology are endurance enhancement and autonomous flight; i.e., mission management and control. This paper proposes a practical method for estimation of true and calibrated airspeed, Angle of Attack (AOA), and Angle of Sideslip (AOS) for small unmanned aerial vehicles (UAVs, up to 20 kg mass, 1200 ft altitude above ground level, and airspeed of up to 100 knots) or light aircraft, for which weight, size, cost, and power-consumption requirements do not allow solutions used in large airplanes (typically, arrays of multi-hole Pitot probes). The sensors used in this research were a static and dynamic pressure sensor (“micro-Pitot tube” MPX2010DP differential pressure sensor) and a 10 degrees of freedom (DoF) inertial measurement unit (IMU) for attitude determination. Kalman and complementary filtering were applied for measurement noise removal and data fusion, respectively, achieving global exponential stability of the estimation error. The methodology was tested using experimental data from a prototype of the devised sensor suite, in various indoor-acquisition campaigns and laboratory tests under controlled conditions. AOA and AOS estimates were validated via correlation between the AOA measured by the micro-Pitot and vertical accelerometer measurements, since lift force can be modeled as a linear function of AOA in normal flight. The results confirmed the validity of the proposed approach, which could have interesting applications in energy-harvesting techniques.

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A comprehensive survey on the methods of angle of attack measurement and estimation in UAVs

Cited by 32 publications

References 42 publications

Wind and Airflow Angle Estimation Using an Adaptive Extended Rauch-Tung-Striebel Smoother

Wind and Airflow Angle Estimation Using an Adaptive Extended Rauch-Tung-Striebel Smoother

The requirements for automation systems based on Boeing 737 MAX crashes

Estimation of Airspeed, Angle of Attack, and Sideslip for Small Unmanned Aerial Vehicles (UAVs) Using a Micro-Pitot Tube

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