Improved State Space Model Using Iterative PSO for Unsteady Aerodynamic System at High AOA

Guiming Luo, Boxu Zhao and Mengqi Jiang
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Guiming Luo: School of Software, Tsinghua University, Beijing, China
Boxu Zhao: School of Software, Tsinghua University, Beijing, China
Mengqi Jiang: School of Software, Tsinghua University, Beijing, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2018, vol. 12, issue 3, 1-17

Abstract: Due to the complex hysteresis phenomenon at a high angle of attack (AOA), modeling of unsteady aerodynamic coefficients usually encounters the problem that the parameter vector is too long and the simulation accuracy is not high. The article proposes an improved state-space model based on aerodynamics, applying Fourier analysis and the principal component analysis for model optimization. The likelihood criterion and GOIPSO (Iterative Particle Swarm Optimization Based on Genetic Operator) algorithm are established under the Gaussian assumption. The iterative PSO, into which the genetic algorithm's operators are integrated to calculate the optimization of the likelihood function, greatly reduced the probability of local optimization. Experiments show that the algorithm and model proposed in this paper greatly improves the model-fitting accuracy.

Date: 2018
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