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

Taotao QIN; Siyuan JI; Lin LEI; Zhanfeng ZHENG

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

更新时间：2025-09-05

- Non-parametric Modelling and Muzzle Velocity Prediction of Multi-stage Induction Coilgun based on PSO-RNN Algorithm
- Acta Armamentarii Vol. 46, Issue 7, Pages: 240616(2025)
- 作者机构：
  
  1. 南京理工大学瞬态物理全国重点实验室, 江苏南京 210094
  2. 国网山东省电力公司枣庄供电公司, 山东枣庄 277000
  3. 国网电力科学研究院有限公司, 江苏南京 210014
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0616
  CLC：
- Received：23 July 2024，
  
  Online First：12 August 2025，
  
  Published：31 July 2025
- 稿件说明：
移动端阅览
Taotao QIN, Siyuan JI, Lin LEI, et al. Non-parametric Modelling and Muzzle Velocity Prediction of Multi-stage Induction Coilgun based on PSO-RNN Algorithm[J]. Acta Armamentarii, 2025, 46(7): 240616.
DOI：

Taotao QIN, Siyuan JI, Lin LEI, et al. Non-parametric Modelling and Muzzle Velocity Prediction of Multi-stage Induction Coilgun based on PSO-RNN Algorithm[J]. Acta Armamentarii, 2025, 46(7): 240616. DOI： 10.12382/bgxb.2024.0616.

摘要

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.

关键词

Keywords

references

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