Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Armanini, Sophie F.; Karásek, Matěj; Visser, Coen C. de; Croon, Guido de; Mulder, Max

doi:10.2514/6.2017-1634

Cited by 8 publications

(21 citation statements)

References 43 publications

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“…33 Flight tests were intended for identification of the longitudinal dynamics, and involved elevator input signals, executed by the autopilot when triggered by the pilot. 34 The hardware and flight test setup, data processing and sensor fusion process are described in more detail in Refs. 35 and 34 -the basic process was analogous for this study, however new tests were conducted involving a larger number of manoeuvres, and incorporating minor practical improvements based on the previous flight test campaigns.…”

Section: Iib Flight Data Acquisitionmentioning

confidence: 99%

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Global LPV model identification of flapping-wing dynamics using flight data

Armanini

Karásek

Visser

2018

2018 AIAA Modeling and Simulation Technologies Conference

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Biologically-inspired flapping-wing micro aerial vehicles are characterised by nonlinear, unsteady aerodynamics and complex dynamics, both highly challenging to model. To take full advantage of the flight capabilities of such vehicles, it is necessary to obtain insight into their dynamics in the different flyable conditions, and to provide adequate control in all of these conditions. Nonetheless, the dynamics are typically only considered in a single flight regime, and controllers are frequently tuned for a particular flight condition. Due the high complexity of flapping flight and limited availability of accurate free-flight data, global models are not yet readily available, particularly models based on free-flight data and suitable for practical applications. This paper demonstrates an approach to obtain a global dynamic model for a flapping-wing micro aerial vehicle. To allow for standard linear control and systems theory to be applied, the nonlinear dynamics are approximated using a linear parameter-varying (LPV) approach based on a set of local linear models. The scheduling parameters, and the parameters in the underlying local models, are determined using system identification methods applied to free-flight data collected on a real test platform, and covering a significant part of the flight envelope. The proposed approach allows for modelling of the vehicle and prediction of the dominant dynamic properties across the considered part of the flight envelope, using a total of 16 parameters, as opposed to the starting point of 46 local models with 12 parameters each. The use of a single model adapting to the flight condition provides flexibility and continuous coverage, and is therefore highly useful for simulation and control applications. While in the explored part of the flight envelope the nonlinearity was found to be limited, such that a weighted average model may be sufficient for some applications, the LPV model provides a higher accuracy and more consistent performance across the conditions considered. Additionally, the approach is shown to be promising and is expected to be adaptable to cover more significant variation. Improvements could be obtained through more extensive flight envelope coverage, more accurate measurement and more informative identification data.

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Section: Iib Flight Data Acquisitionmentioning

confidence: 99%

“…35 and 34 -the basic process was analogous for this study, however new tests were conducted involving a larger number of manoeuvres, and incorporating minor practical improvements based on the previous flight test campaigns. 35,34…”

Section: Iib Flight Data Acquisitionmentioning

confidence: 99%

Global LPV model identification of flapping-wing dynamics using flight data

Armanini

Karásek

Visser

2018

2018 AIAA Modeling and Simulation Technologies Conference

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“…These can be easily programmed and executed, are relatively rapid, can be used to excite several axes simultaneously if applied to different actuators in sequence, and can be tailored to the system and setup with relative ease, even when the dynamics are not yet well known. Through extensive flight testing we found such signals to provide suitable excitation, while presenting fewer challenges than frequency seep [17], [20].…”

Section: A Identification Maneuvers/ Input Designmentioning

confidence: 99%

“…In many studies, sub-flap cycle effects are neglected when dealing with the dynamics: in this case, the flapping-related oscillations can be considered a disturbance, which may lead to a low signal-to-noise ratio and adversely affect the identification process. Such oscillations may be significant and the effect may have to be countered using additional low-pass filtering [17], [20] or larger than usual input signals [20] -the former is preferable when linear models are used, as the latter may conflict with possible linearity requirements.…”

Section: A Identification Maneuvers/ Input Designmentioning

confidence: 99%

“…However, novel systems like FWMAVs still constitute a challenge due to their high complexity, the more limited knowledge we have of them, and the limited flight experience and data available. This section summarizes a number of recommendations/guidelines for input design, specifically for small flapping-wing vehicles, based on our flight testing experience, as well as preliminary studies [9], [17], [20], [33].…”

Section: A Identification Maneuvers/ Input Designmentioning

confidence: 99%

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In-flight data acquisition and flight testing for system identification of flapping-wing MAVs

Caetano

Armanini

Karásek

2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-Although flapping-wing micro aerial vehicles have become a hot topic in academia, the knowledge we have of these systems, their force generation mechanisms and dynamics is still limited. Recent technological advances have allowed for the development of free flight test setups using on-board sensors and external tracking systems, for system identification purposes. Nevertheless, there is still little knowledge about the system requirements, as well as on how to perform free flight test experiments, and process the collected data. The present article presents the guidelines for flapping-wing micro aerial vehicle free flight testing. In particular, it gathers information produced by different studies and provides the best practices for the proper system dimensioning, system setup, on-board sensors, maneuver input design, error analyses and data postprocessing, for the reconstruction of the forces and moments that act during free flight of a flapping-wing robot, for system identification and modeling purposes. Furthermore, this article compares the results obtained using external optical position tracking systems with on-board and external sensor fusion, and provides suitable solutions and methods for data fusion and force reconstruction.

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Performance of Low-Cost Air-Data Sensors for Airspeed and Angle of Attack Measurements in a Flapping-Wing Robot

Amini

Ayati

2019

J. Aerosp. Eng.

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Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Cited by 8 publications

References 43 publications

Global LPV model identification of flapping-wing dynamics using flight data

Global LPV model identification of flapping-wing dynamics using flight data

In-flight data acquisition and flight testing for system identification of flapping-wing MAVs

Performance of Low-Cost Air-Data Sensors for Airspeed and Angle of Attack Measurements in a Flapping-Wing Robot

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