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Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (7): 240616-.doi: 10.12382/bgxb.2024.0616

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Non-parametric Modelling and Muzzle Velocity Prediction of Multi-stage Induction Coilgun based on PSO-RNN Algorithm

QIN Taotao1,*(), JI Siyuan1, LEI Lin2, ZHENG Zhanfeng3   

  1. 1 National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
    2 Zaozhuang Power Supply Company, State Grid Shandong Electric Power Company, Zaozhuang 277000, Shandong, China
    3 State Grid Electric Power Research Institute Co., Ltd., Nanjing 210014, Jiangsu, China
  • Received:2024-07-23 Online:2025-08-12
  • Contact: QIN Taotao

Abstract:

An non-parametric model of multi-stage synchronous induction coilgun (MSSICG) based on the particle swarm optimization and recurrent neural network (PSO-RNN) algorithm is proposed to solve the problems such as multi-physics field coupling and long iteration time of existing optimization methods.And the ejection velocity of the armature is also predicted by the model.A sample set with the turns per coil,triggering time and trigger position as inputs and the ejection velocity as output is obtained through the orthogonal and random experiments.The RNN algorithm is used to train the sample set and the non-parametric model is established.The parameters of the RNN model are further optimized by the PSO algorithm,to improve the prediction performance of the non-parametric model.The ejection velocity of the armature is predicted using the proposed PSO-RNN model and compared with the experimental result.The MSPE,MAPE,and RMSE of the non-parametric model are 0.0028,0.036,and 2.18,respectively,which are reduced by 39%,38%,and 46% after the optimization of PSO.The difference between the predicted and experimental velocities is 1.2m/s with the error percentage of 1.8%,which is less than 5%.The study provides a novel idea for the modelling and engineering design of MSSICG.

Key words: multi-stage synchronous induction coilgun, non-parametric model, recurrent neural network, particle swarm optimization, muzzle velocity

CLC Number: