Micro Air Vehicle’s Attitude Control Using Real-Time Pressure and Shear Information

Shen, He; Xu, Yunjun; Dickinson, Benjamin T.

doi:10.2514/1.c032375

Cited by 24 publications

(16 citation statements)

References 16 publications

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“…It has also been demonstrated that wing mounted arrays can be used to estimate non-conventional parameters such as lift, drag and pitching moment [15,16]. These estimates could potentially be used in a physics based control approach where the total forces and moments acting on the aircraft are estimated based on signals from distributed sensors [19].…”

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

“…These results suggest that pressure and strain sensing could be used in combination with ANN-based estimators to improve flight control performance, by providing accurate estimates of aerodynamic variables and loads. The aerodynamic variables could be fed into model-based controllers, or the aerodynamic loads could be used directly by physics-based controllers [18,27,28]. Alternatively, a more robust estimate of the aircraft's dynamic state can be obtained by fusing the signals from the distributed array with the inertial and visual information of conventional sensors.…”

Section: Discussionmentioning

confidence: 99%

Aerodynamic Variables and Loads Estimation Using Bio-Inspired Distributed Sensing

Araujo-Estrada

Windsor

2019

AIAA Scitech 2019 Forum

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Conventional control systems for autonomous aircraft use a small number of precise sensors encoding centre of mass motion and generally are setup for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Flying animals in contrast take advantage of highly non-linear structural dynamics and aerodynamics to achieve efficient and robust flight control. It appears that the distributed arrays of flow and force sensors found in flying animals play a key roll in enabling their remarkable flight control. This paper presents current research using a wing model instrumented with distributed arrays of load and flow sensors to provide estimates of a range of aerodynamic and load related variables. The characteristics of instrumentation on the wing model, as well as those of a 1-DOF pitch rig are described and characterisation experiments carried out in a closed circuit low turbulence wind tunnel are presented. The results from these experiments show that a wealth of information can be extracted from the pressure and strain signals, including the state of the flow around the wing, and rate dependent non-linear structural and aerodynamic behaviour over a wide range of angles of attack, including well into the stall region. Using the signals from the distributed array Artificial Neural Networks were trained to provide estimates of angle of attack, airspeed, drag, lift and pitching moment. The networks were able to accurately estimate α (RMS error 0.15°), airspeed (RMS error 0.15 m/s-1.25% Full-Scale-Error (FSE)), drag (RMS error 0.33 N-1.78%FSE), lift (RMS error 0.57 N-0.60%FSE) and pitching moment (RMS error 0.03 N m-5.00%FSE). These estimators provided good estimates even in the stall region when the distributed array pressure and strain signals became unsteady. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase manoeuvrability and improved control of UAVs with high degrees of freedom such as highly flexible or morphing wings. Nomenclature Roman Symbols D Aerodynamic drag force, N L Aerodynamic lift force, N M Aerodynamic pitching moment, N m c Wing model mean aerodynamic chord, m q Wing model pitch rate,°/s b Wing model span, m S Wing model reference surface, m 2 V Wind speed, m/s Greek Symbols

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“…This type of distributed information could be integrated as part of a physics based control approach, being based on the estimating the total forces and moments acting on the aircraft. [8][9][10] Or alternatively this information could be used to achieve a more robust estimate of the aircrafts dynamic state, where the state is estimated based on fusing the signals from a number of different sensors rather than a single sensor. This approach has many similarities to the "mode sensing" approach hypothesized to be used by insects, 5 but raises the question as to what are the appropriate states to measure when fusing information from distributed sensors or from different sensory modalities.…”

Section: Discussionmentioning

confidence: 99%

“…In some cases the pressure is integrated over the surface to provide measurements of the forces and/or moments acting on the aircraft. [8][9][10] While in other cases the sensors comprise part of a flush air data system used to give estimates of aerodynamic state variables such as air speed, angle of attack and sideslip. 11,12 There have also been investigations into the potential advantages offered by unconventional flow sensors such as artificial hair based flow velocity sensors.…”

Section: Introductionmentioning

confidence: 99%

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

Araujo-Estrada¹,

Salama²,

Greatwood³

et al. 2017

AIAA Guidance, Navigation, and Control Conference

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Micro Air Vehicle’s Attitude Control Using Real-Time Pressure and Shear Information

Cited by 24 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

Aerodynamic Variables and Loads Estimation Using Bio-Inspired Distributed Sensing

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

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