Dynamic aerodynamic parameter estimation using a dynamic particle swarm optimization algorithm for rolling airframes

The aerodynamic parameters of each flying vehicle dynamically change along its flight profile, because of aerodynamic parameter relationship with flight conditions, and several flight conditions take place during each flight profile. Therefore, in this research, the concept of dynamic aerodynamic pa...

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Vydáno v:Journal of the Brazilian Society of Mechanical Sciences and Engineering Ročník 42; číslo 11
Hlavní autoři: Mohamad, Ayham, Karimi, Jalal, Naderi, Alireza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
Témata:
Aerodynamic forces
Aerodynamics
Airframes
Engineering
Evolutionary algorithms
Extended Kalman filter
Flight conditions
Mechanical Engineering
Optimization algorithms
Parameter estimation
Particle swarm optimization
Technical Paper
Particle swarm optimization
Smoothing filter
Dynamic aerodynamic parameter estimation
Dynamic optimization
Estimation after modeling
ISSN:1678-5878, 1806-3691
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Shrnutí:The aerodynamic parameters of each flying vehicle dynamically change along its flight profile, because of aerodynamic parameter relationship with flight conditions, and several flight conditions take place during each flight profile. Therefore, in this research, the concept of dynamic aerodynamic parameter estimation (DAPE) is introduced. A two-step strategy is used: In the first step, the aerodynamic forces and moments are estimated; then, after passing through a designed smoothing filter, in the second step, the DAPE is converted to a dynamic optimization problem and solved by a heuristic optimization algorithm that hybridizes the features of particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. Two new algorithms are developed: DAPE and SDAPE. In DAPE algorithm, all aerodynamic parameters are estimated at once by solving a single optimization problem. In SDAPE algorithm, four separate optimization problems are solved. A rolling airframe is the plant studied in this research. Simulation results indicate that SDAPE is better than DAPE in terms of accuracy. Comparing the performance of the newly proposed algorithms with that of three state-of-the-art static optimization algorithms and extended Kalman filter reveals their less run time and acceptable accuracy.
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ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-020-02658-y