Gust rejection using force adaptive feedback for roll

Castano, Lina; Airoldi, Simone; McKenna, Terrence; Humbert, J. Sean

doi:10.2514/6.2014-2588

Cited by 14 publications

(9 citation statements)

References 11 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of distributed force or strain measurement for flight control is a less well-investigated area than distributed flow sensing. Initial work has shown the potential for this type of sensor information to improve flight control, with the potential for faster responses [21] and the use of a physics-based control approach [22]. Previous studies have looked at the advantages of each system in isolation in separate aircraft [23] but, as yet, the potential advantages of using both distributed airflow and load information in a single aircraft have not been well explored.…”

mentioning

confidence: 99%

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Araujo-Estrada

Windsor

2021

Journal of Aircraft

View full text Add to dashboard Cite

mentioning

confidence: 99%

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Araujo-Estrada

Windsor

2021

Journal of Aircraft

View full text Add to dashboard Cite

“…Using the wing model and testing rig described in Section II, a series of characterisation experiments were carried out in the University of Bristol low turbulence wind tunnel [22]. Quasi-static as well as dynamic tests were performed at air speeds V = [8,10,12,14,16,18,20] m/s to characterise the aerodynamic loads, pressure and strain signals. These tests consisted of α sweeps at various pitch rates.…”

Section: Signal Characterisation Resultsmentioning

confidence: 99%

“…The use of distributed force or strain measurement for flight control is a less well investigated area than distributed flow sensing. Initial work has shown the potential for this type of sensor information to improve flight control, with the potential for faster responses [19] and the use of a physics based control approach [20]. Previous studies have looked at the advantages of each system in isolation in separate aircraft [21], but as yet the potential advantages of utilizing both distributed airflow and load information in a single aircraft have not been well explored.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Variables and Loads Estimation Using Bio-Inspired Distributed Sensing

Araujo-Estrada

Windsor

2019

AIAA Scitech 2019 Forum

View full text Add to dashboard Cite

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

show abstract

“…The use of distributed force or strain measurement for flight control is a less well investigated area than distributed flow sensing. 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

View full text Add to dashboard Cite

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

show abstract

Gust rejection using force adaptive feedback for roll

Cited by 14 publications

References 11 publications

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Aerodynamic State and Loads Estimation Using Bioinspired Distributed Sensing

Aerodynamic Variables and Loads Estimation Using Bio-Inspired Distributed Sensing

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

Contact Info

Product

Resources

About