Fixed-wing MAV attitude stability in atmospheric turbulence—Part 2: Investigating biologically-inspired sensors

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

doi:10.1016/j.paerosci.2014.06.002

Cited by 78 publications

(53 citation statements)

References 102 publications

(113 reference statements)

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“…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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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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“…While the time scales of adaptive control are by definition longer than the dynamical response times of vehicles, adaptive control can be used to tune real-time controllers [38,80,81] and are, therefore, relevant to vehicle learning. Some modern flying vehicles already use arrays of lowbandwidth anemometers for flow sensing [16,82,83]. These sensor arrays can be, for example, shear stress sensors embedded in a flexible skin [84], pressure ports distributed over the body or wings [56,[84][85][86], or hot-film sensors distributed over the wing [87].…”

Section: Convergence Is Accelerated By Multiple Lowbandwidth Anemometersmentioning

confidence: 99%

Adaptive control of turbulence intensity is accelerated by frugal flow sampling

Quinn¹,

Halder²,

Lentink³

2017

J. R. Soc. Interface.

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The aerodynamic performance of vehicles and animals, as well as the productivity of turbines and energy harvesters, depends on the turbulence intensity of the incoming flow. Previous studies have pointed at the potential benefits of active closed-loop turbulence control. However, it is unclear what the minimal sensory and algorithmic requirements are for realizing this control. Here we show that very low-bandwidth anemometers record sufficient information for an adaptive control algorithm to converge quickly. Our online Newton-Raphson algorithm tunes the turbulence in a recirculating wind tunnel by taking readings from an anemometer in the test section. After starting at 9% turbulence intensity, the algorithm converges on values ranging from 10% to 45% in less than 12 iterations within 1% accuracy. By down-sampling our measurements, we show that very-low-bandwidth anemometers record sufficient information for convergence. Furthermore, down-sampling accelerates convergence by smoothing gradients in turbulence intensity. Our results explain why low-bandwidth anemometers in engineering and mechanoreceptors in biology may be sufficient for adaptive control of turbulence intensity. Finally, our analysis suggests that, if certain turbulent eddy sizes are more important to control than others, frugal adaptive control schemes can be particularly computationally effective for improving performance.

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“…In nature, feathers act as the deformable membrane, which vibrate or deflect during flight due to the airflow (Necker, ). This biological sensory system enables a bird to “feel” a number of phenomenon, such as the progression of flow separation over its wings during aggressive maneuvers and the frequency of vibration of its primary and secondary feathers to detect airspeed (Hörster, , ; Mohamed et al., ; Necker, , ). Further research hypothesized the role of the leading‐edge feathers in detecting gusts as sensory feedback to the motor control of birds (Carruthers, Thomas, & Taylor, ).…”

Section: Phase‐advanced Sensingmentioning

confidence: 99%

“…This can be problematic for MAVs carrying vibration‐sensitive payloads, which require a stable platform. Recently, work has been undertaken to identify new sensors capable of measuring turbulence directly rather than the MAV's response to it (Mohamed et al., ). An effective sensor would need to measure the turbulence upstream of the wing at a distance sufficient to offset the time‐lag inherent in the control system.…”

Section: Introductionmentioning

confidence: 99%

Development and Flight Testing of a Turbulence Mitigation System for Micro Air Vehicles

et al. 2015

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There are significant challenges associated with the flight control of fixed-wing micro air vehicles (MAVs) operating in complex environments. The scale of MAVs makes them particularly sensitive to atmospheric disturbances thus limiting their ability to sustain controlled flight. Bio-inspired, phase-advanced sensors have been identified as promising sensory solutions for complementing current inertial-only attitude sensors. This paper describes the development and flight testing of a bio-inspired, phase-advanced sensor and associated control system that mitigates the impact of turbulence on MAVs. Multihole pressure probes, inspired by the sensory function of bird feathers, are used to measure the flow pitch angle and velocity magnitude ahead of the MAV's wing. The sensors provide information on the disturbing phenomena before it causes an inertial response in the aircraft. The sensor output is input to a simple feed-forward control architecture, which enables the MAV to generate a mitigating response to the turbulence. The results from wind-tunnel and outdoor testing in high levels of turbulence are presented. The disturbance rejection performance of the phase-advanced sensory system is compared against that of a conventional inertial-based control system. The developed sensory system shows significant improvement in terms of disturbance rejection performance compared to that of standard inertialonly control system. It is concluded that a phase-advanced sensory systems can complement conventional inertial-based sensors to improve the attitude-tracking performance of MAVs. C 2015 Wiley Periodicals, Inc.

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Fixed-wing MAV attitude stability in atmospheric turbulence—Part 2: Investigating biologically-inspired 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 78 publications

References 102 publications

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

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

Adaptive control of turbulence intensity is accelerated by frugal flow sampling

Development and Flight Testing of a Turbulence Mitigation System for Micro Air Vehicles

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