Nonlinear aerodynamic model identification using empirical mode decomposition

Bagherzadeh, Seyed Amin; Sabzeparvar, Mehdi; Karrari, M.

doi:10.1177/0954410014556112

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Subsequent studies have attempted to employ the isolated modes to improve the aircraft system identification. For example, a conceptual aerodynamic model was proposed by Bagherzadeh et al 20 based on the identification of flight modes for the prediction of aerodynamic forces and moments at high angles of attack and angular rates. Furthermore, the available aircraft system identification methods such as the neural networks are enhanced by considering flight modes.…”

Section: The Extraction Of Flight Modes From Multicomponent Signals Omentioning

confidence: 99%

Flight dynamics modeling of elastic aircraft using signal decomposition methods

Bagherzadeh

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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To improve the precision and accuracy of the flight dynamic models for elastic aircraft, this paper provides a novel method that extracts observable flight modes from flight test data and uses them in the identification process. For this purpose, a gray-box time-domain method is employed with the nonlinear ARX structure and the Levenberg–Marquardt parameter estimation technique. In the proposed method, the components of the flight parameters are extracted by two signal decomposition techniques, namely the singular spectrum analysis and empirical mode decomposition. These components are inputted into the identification process. Flight test data of the active aeroelastic wing is examined by the proposed method. The results indicate that both the singular spectrum analysis-based and empirical mode decomposition-based identification processes have desired performances in dealing with nontrained flight conditions. Thus, these methods may be utilized as an interpolation method to estimate the aircraft flight dynamics within the flight envelope. However, the empirical mode decomposition outperforms the singular mode decomposition because it is more significant in decomposing flight parameters based on the frequency content. Hence, the empirical mode decomposition may be a better tool to be employed for the aeroelastic aircraft system identification.

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Section: The Extraction Of Flight Modes From Multicomponent Signals Omentioning

confidence: 99%

Flight dynamics modeling of elastic aircraft using signal decomposition methods

Bagherzadeh

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…It determines the accuracy of the dynamic model. In the previous studies for multirotor UAVs, the input variables of the filter are usually chosen as the control inputs, 24,25 while the output variables are chosen as the angular rates, accelerations, and so on. However, the dynamic model established by the above input–output variables cannot directly reflect the characteristics of the forces and moments.…”

Section: Introductionmentioning

confidence: 99%

A dynamic model parameter identification method for quadrotors using flight data

Lyu

Bao

Lai

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.

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“…Particularly, in Goodarzi and Sabzehparvar, 28 understanding of the normal flight regime was the main subject of the HHT. Bagherzade and Sabzehparvar 30 concentrated on an online sifting method for the EMD and using this approach for the detection of aircraft damage, and Bagherzadeh et al 31 proposed aerodynamic model using EMD technique and HARMAX algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling

Mokhtari

Sabzehparvar

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Identification of the “aircraft aerodynamic model” in some unusual flight conditions such as spin maneuver provides critical information to the flight controller to retake the “dynamic stability” after it has been disturbed by the systematic, natural or environmental oscillations. Hence, a method for identifying the appropriate aerodynamic model in spin maneuvers is presented in this paper. We present an innovative systematic method for aerodynamic modeling of spin maneuvers, which combines the ensemble empirical mode decomposition technique and extended multipoint modeling approach, using flight data. In ensemble empirical mode decomposition, in addition to having all the empirical mode decomposition features, the original signal is collected with the white noise, and by using its statistical characteristics, the ensemble empirical mode decomposition solves the mode mixing problem. By applying the ensemble empirical mode decomposition to the flight parameter data, their intrinsic mode frequencies are extracted and are used as inputs to the extended multipoint modeling model. The extended multipoint modeling structure includes some parameters describing the distribution of aerodynamic forces and moments along each of the aircraft components. Moreover, this method allows coupling between the forces and moments. Unlike conventional methods, which consider the average forces obtained by plane surfaces relative to the center of mass, in the extended multipoint modeling technique, the force generated by each plane of the aircraft is allowed to appear independently in the motion equations. For identifying the aerodynamic model with extended multipoint modeling structure, the equation error method is used with a maximum likelihood optimizer inside. The obtained algorithm has been applied to two sets of spin maneuver flight data which were recorded in actual spin flight. The results demonstrate that the proposed method is able to reproduce the aerodynamic forces and moments for the second spin flight inputs with high accuracy by using a model which is derived from the first spin data identification.

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Nonlinear aerodynamic model identification using empirical mode decomposition

Cited by 7 publications

References 33 publications

Flight dynamics modeling of elastic aircraft using signal decomposition methods

Flight dynamics modeling of elastic aircraft using signal decomposition methods

A dynamic model parameter identification method for quadrotors using flight data

Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling

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