Fixed-wing MAV attitude stability in atmospheric turbulence, part 1: Suitability of conventional sensors

Abdel‐Rohman, Mohamed; Clothier, Reece; Watkins, Simon; Sabatini, Roberto; Abdulrahim, Mujahid

doi:10.1016/j.paerosci.2014.06.001

Cited by 57 publications

(37 citation statements)

References 74 publications

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“…Current control systems for small UAS typically make use of several traditional sensors. Namely a 6-degree-of-freedom (DoF) inertial measurement unit (IMU), a GPS receiver, a magnetometer, an anemometer, and possibly an altimeter [2]. These avionics systems are well developed and work well for flight in simple conditions.…”

Section: Introductionmentioning

confidence: 99%

Distributed Pressure Sensing–Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

Wood

Araujo-Estrada

Richardson

et al. 2019

Journal of Aircraft

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Small fixed-wing Unmanned Aerial Systems (UAS) might require increased agility when operating in turbulent wind fields. In these conditions, conventional sensor suites could be augmented with additional flow-sensing to extend the aircraft's usable flight envelope. Inspired by distributed sensor arrays in biological systems, a UAS with a chord-wise array of pressure sensors was developed. Wind-tunnel testing characterised these sensors alongside a conventional airspeed sensor and an angle-of-attack (AoA) vane, and showed a single pressure measurement gave a linear response to AoA pre-stall. Flight tests initially manually piloted the vehicle through pitching manoeuvres, then in a series of automated manoeuvres based on closed-loop feedback using an estimate of AoA from the single pressure port. The AoA estimate was successfully used to control the attitude of the aircraft. An Artificial Neural Network (ANN) was trained to estimate the AoA and airspeed using all pressure ports in the array, and validated using flight-trial data. The ANN more accurately estimated the AoA over a single port method with good robustness to stall and unsteady flow. Distributed flow sensors could be used to supplement conventional flight control systems, providing enhanced information about wing flow conditions with application to systems with highly flexible or morphing wings.

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Section: Introductionmentioning

confidence: 99%

Distributed Pressure Sensing–Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

Wood

Araujo-Estrada

Richardson

et al. 2019

Journal of Aircraft

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show abstract

“…Another potential benefit of load monitoring is stable and efficient control of an aircraft, including unmanned air vehicles (UAVs). Considering the gust perturbation process for example, the load variations from oncoming gusts result in attitude and flight path variations, and there is a theoretical phase-lag between the cause (load) and effect (attitude and flight path) [5][6][7]. The load is a "phase-advanced" phenomena in this sense, and, therefore, its observation in addition to the conventional inertia observations could potentially enhance vehicle controllability.…”

Section: Introductionmentioning

confidence: 99%

Wing Load and Angle of Attack Identification by Integrating Optical Fiber Sensing and Neural Network Approach in Wind Tunnel Test

Wada

Tamayama

2019

Applied Sciences

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The load and angle of attack (AoA) for wing structures are critical parameters to be monitored for efficient operation of an aircraft. This study presents wing load and AoA identification techniques by integrating an optical fiber sensing technique and a neural network approach. We developed a 3.6-m semi-spanned wing model with eight flaps and bonded two optical fibers with 30 fiber Bragg gratings (FBGs) each along the main and aft spars. Using this model in a wind tunnel test, we demonstrate load and AoA identification through a neural network approach. We input the FBG data and the eight flap angles to a neural network and output estimated load distributions on the eight wing segments. Thereafter, we identify the AoA by using the estimated load distributions and the flap angles through another neural network. This multi-neural-network process requires only the FBG and flap angle data to be measured. We successfully identified the load distributions with an error range of −1.5–1.4 N and a standard deviation of 0.57 N. The AoA was also successfully identified with error ranges of −1.03–0.46° and a standard deviation of 0.38°.

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“…Fixed-wing UA's susceptibility to rolling moments is brought about by spanwise variations in angle of attack and velocity (Abdulrahim, Watkins, Segal, Marino, & Sheridan, 2010;Etkin, 2005;Mohamed, Clothier, Watkins, Sabatini, & Abdulrahim, 2014;Mohamed, Massey et al, 2014;Nelson, 1998;Stevens & Lewis, 2003;Watkins et al, 2006). Inspection of the basic lift equation, given in Equation 3 where L represents the force of lift and S the wing planform area, show that, neglecting any changes in air density, ρ, that might be brought about by turbulence, only changes in velocity, v, or the lift coefficient, CL, change the amount of lift produced.…”

Section: Affect Of the Environment On Suasmentioning

confidence: 99%

Urban flow and small unmanned aerial system operations in the built environment

Adkins¹

2019

IJAAA

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Fixed-wing MAV attitude stability in atmospheric turbulence, part 1: Suitability of conventional sensors

Abstract: This is the authors' pre-publication version. This paper does not include changes and revisions arising from the peer review and publishing processes. The final definitive copy, which should be used for all referencing,

Cited by 57 publications

References 74 publications

Distributed Pressure Sensing–Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

Distributed Pressure Sensing–Based Flight Control for Small Fixed-Wing Unmanned Aerial Systems

Wing Load and Angle of Attack Identification by Integrating Optical Fiber Sensing and Neural Network Approach in Wind Tunnel Test

Urban flow and small unmanned aerial system operations in the built environment

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