Identification of Spinning Projectile Aerodynamic Parameters Using Adaptive Chaotic Mutation Particle Swarm Optimization

doi:10.3969/j.issn.1000-1093.2017.01.010

Abstract

Abstract: The maximum likelihood estimation is applied to the identification of spinning projectile aerodynamic parameters. A new algorithm called adaptive chaotic mutation particle swarm optimization is proposed to solve the optimal solution of aerodynamic parameters, thus obtaining the aerodynamic parameters of a spinning projectile. The proposed algorithm is to use an adaptive weight function, generate the initial particles based on chaos theory, and set a discriminant mechanism which judges whether the algorithm falls into the local optimum. If the algorithm falls into the local convergence, the mutation operator is used to make the algorithm jump out of local. The common test function is used to test this algorithm. The test result shows that the proposed algorithm has the advantages of more quick convergence, higher optimization precision and wide range of application compared to basic PSO. Simulated ballistic data is used to test the algorithm. The result shows that the proposed algorithm can identify the aerodynamic parameters effectively with high precision and quickly converging velocity. Key

Key words: ordnancescienceandtechnology, projectile, aerodynamicparametersidentification, particleswarmoptimizationalgorithm, maximumlikelihoodestimation

CLC Number:

TJ012.3⁺4

GUAN Jun， ZHOU Jia-shen, YI Wen-jun， LIU Shi-ping， CHANG Si-jiang， SHI Ji-gang. Identification of Spinning Projectile Aerodynamic Parameters Using Adaptive Chaotic Mutation Particle Swarm Optimization[J]. Acta Armamentarii, 2017, 38(1): 73-80.

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第38卷
第1期2017 年1月兵工学报ACTA
ARMAMENTARIIVol.38No.1Jan.2017

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