Flight Vehicle System Identification

Jategaonkar, R.V.

doi:10.2514/4.866852

Cited by 387 publications

(100 citation statements)

References 34 publications

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“…The flight test was done with a specific flight test maneuver that is necessary to excite certain attitude of the UAV without exciting the other [4]. The following flight test pattern is executed successfully:…”

Section: Resultsmentioning

confidence: 99%

“…The best input to be given into a black box is an impulse because the resulting output from an impulse input is the exact representation of the system as illustrated by the following figure. FIGURE 3. Impulse input and system identification [4] For this study, an input that is close enough to an impulse is used and as the UAV is using multiple inputs, each of the inputs will be excited separately while the others are kept at idle or zero as much as possible. The inputs will come from the radio controller command stick and the output will be the attitude measurement from the autopilot.…”

Section: Methodology Uav As a Black Boxmentioning

confidence: 99%

See 1 more Smart Citation

Intification and modelling of flight characteristics for self-build shock flyer type UAV

Rashid¹,

Dardin²,

Azid³

et al. 2018

AIP Conference Proceedings

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Abstract. The development of an autonomous Unmanned Aerial Vehicle (UAV) requires a fundamentals studies of the UAV's flight characteristic. The aim of this study is to identify and model the flight characteristic of a conventional fixed-wing type UAV. Subsequence to this, the mode of flight of the UAV can be investigated. One technique to identify the characteristic of a UAV is a flight test where it required specific maneuvering to be executed while measuring the attitude sensor. In this study, a simple shock flyer type UAV was used as the aircraft. The result shows that the modeled flight characteristic has a significant relation with actual values but the fitting value is rather small. It is suggested that the future study is conducted with an improvement of the physical UAV, data filtering and better system identification methods.

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Section: Resultsmentioning

confidence: 99%

Section: Methodology Uav As a Black Boxmentioning

confidence: 99%

Intification and modelling of flight characteristics for self-build shock flyer type UAV

Rashid¹,

Dardin²,

Azid³

et al. 2018

AIP Conference Proceedings

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“…Most used parameter estimation methods are: regression analysis (least squares method, LSMortotal least squares method, TLSM) and maximum likelihood method (MLE) [14,17,19].…”

Section: Parametric Aircraft Modelmentioning

confidence: 99%

Aircraft performance monitoring from flight data

2015

Teh. vjesn.

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Preliminary notesTo ensure timely maintenance and efficient aircraft operations, it is necessary to know and keep track of aircraft's actual performance. Flight performance is determined by aircraft's physical characteristics. Theoretical aircraft performance, obtained after manufacturing and flight testing, are described in flight manual. Transport aircraft in operation is usually exposed to standard operational conditions. Despite the standard operational conditions and regular aircraft maintenance, structure aging and high dynamic loads due to high subsonic Mach number could lead to changes of main physical factors that determine flight performance. For this reason actual aircraft performance often differs from theoretical. Commercial airlines monitor true performance of aircraft in operation. This paper presents an overview of existing performance monitoring methods as well as first indications for new research possibilities regarding physical characteristics determination for aircraft in operation using flight data. Keywords: aerodynamic coefficients; flight performance; flight testing; system identification Praćenje stvarnih performansi zrakoplova prema podacima iz letaPrethodno priopćenje Poznavanje stvarnih performansi zrakoplova bitno je za učinkovitu eksploataciju i pravovremeno održavanje. Performanse su određene fizikalnim karakteristikama zrakoplova. U Priručniku za letenje opisane su teorijske performanse određene od proizvođača nakon proizvodnje zrakoplova i testiranja u letu. Komercijalni zrakoplovi su tijekom svog operativnog ciklusa uglavnom izloženi predviđenim uvjetima eksploatacije. Unatoč predviđenim uvjetima eksploatacije i redovnom održavanju, starenje materijala i velika opterećenja na strukturu zrakoplova kod leta visokim podzvučnim Machovim brojem, mogu dovesti do promjene temeljnih fizikalnih faktora koji određuju performanse. Zbog toga se stvarne performanse zrakoplova nerijetko razlikuju od teorijskih. Zračni prijevoznici prate stanje zrakoplova i njegove stvarne performanse tijekom korištenja. U ovom radu prikazan je pregled dosadašnjih metoda praćenja performansi i mogućnosti istraživanja na području određivanja fizikalnih parametara zrakoplova u eksploataciji prema podacima iz leta.Ključne riječi: aerodinamički koeficijenti; identifikacija sustava; ispitivanje u letu; performanse leta

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“…Therefore, in developing system identification techniques, neither off-line nor on-line procedures take into account dynamic stability derivatives (Tischler and Remple 2006;Klein and Morelli 2006;Jategaonkar 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Because of the EKF has been successfully employed for off-line and on-line identification of stability and control derivatives for manned aircraft (Alonge et al 2015;Shim et al 2014, Kokolios 1994Speyer and Crues 1987;Kobayashi et al 2005;Huang et al 1996;Jategaonkar andPlaetenschke 1988, 1989;García-Velo and Walker 1997), by considering the augmented computation capability of on-board UAS, a procedure based on EKF has been implemented.…”

Section: Introductionmentioning

confidence: 99%

An Extended Kalman Filter-Based Technique for On-Line Identification of Unmanned Aerial System Parameters

Grillo¹,

Montano

2015

J.Aerosp. Technol. Manag.

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The present article deals with the identification, at the same time, of aircraft stability and control parameters taking into account dynamic damping derivatives. Such derivatives, due to the rate of change of the angle of attack, are usually neglected. So the damping characteristics of aircraft dynamics are attributed only on pitch rate derivatives. To cope with the dynamic effects of these derivatives, authors developed devoted procedures to estimate them. In the present paper, a complete model of aerodynamic coefficients has been tuned-up to identify simultaneously the whole set of derivatives. Besides, in spite of the employed reduced order model and/or decoupled dynamics, a six degrees of freedom model has been postulated without decoupling longitudinal and lateral dynamics. A recursive non-linear filtering approach via Extended Kalman Filter is proposed, and the filter tuning is performed by inserting the effects of dynamic derivatives into the mentioned mathematical model of the studied aircraft. The tuned-up procedure allows determining with noticeable precision the stability and control derivatives. In fact, either by activating maneuvers generated by all the control surfaces or by inserting noticeable measurement noise, the identified derivatives show very small values of standard deviation. The present study shows the possibility to identify simultaneously the aircraft derivatives without using devoted procedures and decoupled dynamics. The proposed technique is particularly suited for on-line parametrical identification of Unmanned Aerial Systems. In fact, to estimate both state and aircraft parameters, low power and time are required even using measurement noises typical of low-cost sensors.

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Flight Vehicle System Identification

Cited by 387 publications

References 34 publications

Intification and modelling of flight characteristics for self-build shock flyer type UAV

Intification and modelling of flight characteristics for self-build shock flyer type UAV

Aircraft performance monitoring from flight data

An Extended Kalman Filter-Based Technique for On-Line Identification of Unmanned Aerial System Parameters

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