Small low-cost unmanned aerial vehicle system identification: A survey and categorization

Hoffer, Nathan V.; Coopmans, Calvin; Jensen, Austin M.; Chen, YangQuan

doi:10.1109/icuas.2013.6564775

Cited by 18 publications

(7 citation statements)

References 49 publications

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“…Çalışmada ele alan yayınların basım yıllarına göre sayıları, yayın isimleri ve referans numaraları Tablo 1'de verilmiştir. Test Çalışmaları [1], [2], [3], [4], [23], [42], [51] Dizayn ve Modelleme Çalışmaları [1], [2], [6], [11], [14], [18], [20], [23], [24], [25], [26], [27], [31], [35], [36], [38], [39], [40], [41], [42], [45], [46], [47], [49], [50], [51], [52], [53], [56], [57] Survey Çalışmaları [10], [12], [13], [28], [33], [54], [55] Tuning Çalışmaları [3], [35], [48] Simülasyon Çalışmaları [8],…”

Section: Bulgularunclassified

Unmanned Air Vehicles and Autopilots

Akay

Kuriş

Senan

2021

Journal of Aviation Research

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Section: Bulgularunclassified

Unmanned Air Vehicles and Autopilots

Akay

Kuriş

Senan

2021

Journal of Aviation Research

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“…The primary advantage of the TSM is that a variety of model structures can be rapidly examined and assessed when processing test data offline. A review of system identification applications for various types of small fixed-wing UAVs is given in [10].…”

Section: System Identificationmentioning

confidence: 99%

Two-step system identification for control of small UAVs along pre-specified trajectories

Grymin

Farhood

2014

2014 American Control Conference

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This paper presents an approach for regulating a small unmanned aerial vehicle (UAV) about a reference trajectory that is obtained using a model of the aircraft derived from flight test data. Stepwise multiple regression is applied to determine relevant explanatory variables in the aerodynamic coefficient models. The aircraft model is used to obtain a dynamically feasible reference trajectory as the solution of an optimal control problem, solved using pseudospectral optimal control software. A discrete-time feedback controller is then designed to regulate the aircraft along the reference trajectory. Simulations in a realistic operational environment demonstrate the capabilities of the approach.

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“…Hence, the specific maneuvers excited by the changing of the inputs of the actuators definitely improve the observability of each expanded states [ 25 ]. After the categorization of current identification methods for different kinds of aircrafts equipped with different sensors, Hoffer [ 26 ] proposed that the Square-root UKF would be a good choice for the on-line identification. Girish [ 27 ] employs both EKF and UKF to estimate the aerodynamic parameters with the flight data, and it is pointed out that although UKF owns a theoretically better precision, EKF shows the same performance with less computation.…”

Section: Introductionmentioning

confidence: 99%

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs

Jieliang

Liang

2018

Sensors

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The establishment of the Aircraft Dynamic Model (ADM) constitutes the prerequisite for the design of the navigation and control system, but the aerodynamic parameters in the model could not be readily obtained especially for small-scaled fixed-wing UAVs. In this paper, the procedure of computing the aerodynamic parameters is developed. All the longitudinal and lateral aerodynamic derivatives are firstly calculated through semi-empirical method based on the aerodynamics, rather than the wind tunnel tests or fluid dynamics software analysis. Secondly, the residuals of each derivative are proposed to be identified or estimated further via Extended Kalman Filter (EKF), with the observations of the attitude and velocity from the airborne integrated navigation system. Meanwhile, the observability of the targeted parameters is analyzed and strengthened through multiple maneuvers. Based on a small-scaled fixed-wing aircraft driven by propeller, the airborne sensors are chosen and the model of the actuators are constructed. Then, real flight tests are implemented to verify the calculation and identification process. Test results tell the rationality of the semi-empirical method and show the improvement of accuracy of ADM after the compensation of the parameters.

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Small low-cost unmanned aerial vehicle system identification: A survey and categorization

Cited by 18 publications

References 49 publications

Unmanned Air Vehicles and Autopilots

Unmanned Air Vehicles and Autopilots

Two-step system identification for control of small UAVs along pre-specified trajectories

Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs

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