Bio-inspired Distributed Strain and Airflow Sensing for Small Unmanned Air Vehicle Flight Control

Araujo-Estrada, Sergio A.; Salama, Francis; Greatwood, Colin; Wood, Kieran; Richardson, Thomas; Windsor, Shane

doi:10.2514/6.2017-1487

Cited by 17 publications

(16 citation statements)

References 16 publications

(10 reference statements)

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“…Thrust is provided by a 7"×5" propeller driven by small brushless motor with a 20 A electronic speed controller (ESC). From previous research experience [20], the unmodified vehicle is comfortably capable of carrying approximately 400 g of additional payload at flight speeds of approximately 15 m s −1 . In Fig.…”

Section: A Flight Vehiclementioning

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: A Flight Vehiclementioning

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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“…Current on-board sensors record airspeed and trajectory heading, however the exact wind conditions are not measured. There have been recent developments regarding flow sensing in flight, where the wind conditions can be calculated using the differential airspeed sensors [52], distributed pressure sensors [53], and estimated by tracking the drift of the vehicle when circling [54]. As these techniques continue to improve, airspeed matching that facilitates energy harvesting may become more common place too.…”

Section: Energy Savingsmentioning

confidence: 99%

Bird-inspired Velocity Optimization for UAVs in the Urban Environment

Williamson

Spelt²,

Windsor³

2020

AIAA Scitech 2020 Forum

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“…Initial work has shown the potential for this type of sensor information to improve flight control, with the potential for faster responses [23] and the use of a physics based control approach [24]. Previous studies have looked at the advantages of each system in isolation in separate aircraft [25], the potential advantages of utilizing both distributed airflow and load information in combination with Artificial Neural Networks (ANN) to obtain estimates of the aerodynamic variables and loads [21], as well as the use of pressure-based distributed control systems for flight control [26] and gust alleviation [27].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Flight Control Using Distributed Sensors on Fixed-Wing Unmanned Aerial Vehicles

Araujo-Estrada¹,

Windsor²

2020

AIAA Scitech 2020 Forum

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Conventional control systems for autonomous aircraft use a small number of precise sensors in combination with classical control laws to maintain flight. The sensing systems encode center of mass motion and generally are setup for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Gain scheduling is used to overcome some of the limitations from these assumptions, taking advantage of well-tuned controllers over a range of design points. In contrast, flying animals achieve efficient and robust flight control by taking advantage of highly non-linear structural dynamics and aerodynamics. It has been suggested that the distributed arrays of flow and force sensors found in flying animals could be behind their remarkable flight control. Using a wind tunnel aircraft model instrumented with distributed arrays of load and flow sensors, we developed Artificial Neural Network flight control algorithms that use signals from the sensing array as well as the signals available in conventional sensing suites to control angleof-attack. These controllers were trained to match the response from a conventional controller, achieving a level of performance similar to the conventional controller over a wide range of angle-of-attack and wind speed values. Wind tunnel testing showed that by using an ANNbased controller in combination with signals from a distributed array of pressure and strain sensors on a wing, it was possible to control angle-of-attack. The End-to-End learning approach used here was able to control angle-of-attack by directly learning the mapping between control inputs and system outputs without explicitly estimating or being given the angle-of-attack. Nomenclature Roman Symbols b Wing span, m c Wing mean aerodynamic chord, m C P Pressure coefficient e α Angle of attack tracking error,°q Wing model pitch rate,°/s S Wing model reference surface, m 2 t r s rising time, s V Wind speed, m/s Greek Symbols α Angle of attack,°α d Angle of attack demand,°δ e Elevator deflection,°ρ Air density, kg/m 3

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Bio-inspired Distributed Strain and Airflow Sensing for Small Unmanned Air Vehicle Flight Control

Cited by 17 publications

References 16 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

Bird-inspired Velocity Optimization for UAVs in the Urban Environment

Artificial Neural Network-Based Flight Control Using Distributed Sensors on Fixed-Wing Unmanned Aerial Vehicles

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