基于改进PO算法的某供弹机动力学模型参数辨识OA

An ammunition supply machine(ASM)is a complex electromechanical system,and its accurate modeling is the key to conducting reliability analysis and optimization.In order to solve the problem of parameter identification in the dynamics model of the ASM,a dynamic parameter identification method based on the improved parrot optimization(IPO)algorithm was proposed.Firstly,the dynamic model for the ASM was developed,and the Coulomb-viscous friction model was used to describe the friction characteristics in the model.Secondly,to improve the convergence speed and optimization capability of the algorithm,the PO was developed by adaptive step size factors and Cauchy-Gaussian mutation.Finally,based on the experimental data under the conditions of 180° rotation and initial ammunition position,the mathematical model for parameter identification of the ASM was developed.The similarity between the model response curve and the experimental curve of the ASM was taken as the optimization objective.The identification process based on the IPO was determined.The parameters,such as the friction coefficient and reducer efficiency,were identified.The identified parameters were applied to other operating conditions.The results show that the IPO can accurately and effectively identify the parameters in the ASM.The correlation coefficients between the output data of the identified model and the experimental data under other working conditions are all above 0.9,which verifies the accuracy of the model and the effectiveness of the identification results,and provides a theoretical basis for the subsequent reliability analysis and optimization of the ASM.