Simultaneous Multi-step Excitations for Aircraft System Identification

2016 ◽  
Author(s):  
Piotr Lichota ◽  
Per Ohme ◽  
Krzysztof Sibilski
Keyword(s):  
System Identification ◽  
Aircraft System

Nonlinear aeroelastic modeling of aircraft using support vector machine method

2020 ◽  
Vol 92 (3) ◽  
pp. 502-518 ◽  
Author(s):  
Seyed Amin Bagherzadeh
Keyword(s):  
Support Vector Machine ◽  
System Identification ◽  
Support Vector ◽  
Content Type ◽  
System Identification Method ◽  
Identification Method ◽  
Svm Model ◽  
Flight Parameters ◽  
Aircraft System ◽  
Non Parametric

Purpose This paper aims to propose a nonlinear model for aeroelastic aircraft that can predict the flight parameters throughout the investigated flight envelopes. Design/methodology/approach A system identification method based on the support vector machine (SVM) is developed and applied to the nonlinear dynamics of an aeroelastic aircraft. In the proposed non-parametric gray-box method, force and moment coefficients are estimated based on the state variables, flight conditions and control commands. Then, flight parameters are estimated using aircraft equations of motion. Nonlinear system identification is performed using the SVM network by minimizing errors between the calculated and estimated force and moment coefficients. To that end, a least squares algorithm is used as the training rule to optimize the generalization bound given for the regression. Findings The results confirm that the SVM is successful at the aircraft system identification. The precision of the SVM model is preserved when the models are excited by input commands different from the training ones. Also, the generalization of the SVM model is acceptable at non-trained flight conditions within the trained flight conditions. Considering the precision and generalization of the model, the results indicate that the SVM is more successful than the well-known methods such as artificial neural networks. Practical implications In this paper, both the simulated and real flight data of the F/A-18 aircraft are used to provide aeroelastic models for its lateral-directional dynamics. Originality/value This paper proposes a non-parametric system identification method for aeroelastic aircraft based on the SVM method for the first time. Up to the author’s best knowledge, the SVM is not used for the aircraft system identification or the aircraft parameter estimation until now.

Quadrotor aircraft intelligent system identification experiment design

2019 ◽  
Vol 233 (13) ◽  
pp. 4911-4925
Author(s):  
Mohammed Alabsi ◽  
Travis Fields
Keyword(s):  
System Identification ◽  
Power Spectrum ◽  
Intelligent System ◽  
Model Development ◽  
Experiment Design ◽  
Input Power ◽  
Unmanned Aircraft ◽  
Input Design ◽  
Spectrum Optimization ◽  
Aircraft System

Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft model development and prototyping approaches with an efficient real time paradigm. The work presented herein describes the development of an intelligent excitation input design technique that determines excitation frequencies based on predefined rotational motion dynamic model. The input design is then evaluated on quadcopter unmanned aircraft that utilizes the new multisine input design. In order to minimize flight excursions without compromising the modeling capabilities, multisine input power spectrum is optimized based on the vehicle’s frequency response. The proposed methodology emphasizes excitation of modal frequencies which yields flight data rich information content. The generated optimized multisine input design is utilized for a quadcopter aircraft system identification and the performance is compared to conventional uniform amplitudes design. Simulation results show highly accurate model estimation in all identification results in addition to reduction of induced perturbations and power consumption. Additionally, the generated model prediction capabilities are not compromised after power spectrum optimization. Overall, the proposed technique introduces an efficient and intelligent system identification experiment design that can minimize the time and effort spent during excitation input design.

scholarly journals Multi-Axis Inputs for Identification of a Reconfigurable Fixed-Wing UAV

Aerospace ◽  
2020 ◽  
Vol 7 (8) ◽  
pp. 113
Author(s):  
Piotr Lichota
Keyword(s):  
System Identification ◽  
Optimality Criterion ◽  
Likelihood Principle ◽  
Stability And Control ◽  
Input Design ◽  
Relative Standard ◽  
Control Derivatives ◽  
Flight Controls ◽  
Aircraft System ◽  
And Control

Designing a reconfiguration system for an aircraft requires a good mathematical model of the object. An accurate model describing the aircraft dynamics can be obtained from system identification. In this case, special maneuvers for parameter estimation must be designed, as the reconfiguration algorithm may require to use flight controls separately, even if they usually work in pairs. The simultaneous multi-axis multi-step input design for reconfigurable fixed-wing aircraft system identification is presented in this paper. D-optimality criterion and genetic algorithm were used to design the flight controls deflections. The aircraft model was excited with those inputs and its outputs were recorded. These data were used to estimate stability and control derivatives by using the maximum likelihood principle. Visual match between registered and identified outputs as well as relative standard deviations were used to validate the outcomes. The system was also excited with simultaneous multisine inputs and its stability and control derivatives were estimated with the same approach as earlier in order to assess the multi-step design.

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