Optimization of Multistage Coilgun Based on Neural Network and Intelligent Algorithm

Abstract

The parameter optimization of a multistage synchronous induction coilgun (SICG) is a time-consuming task. Traditional machine learning methods can accelerate the process by building predictive models, but they require separate modeling for an SICG with different stages, which requires numerous datasets and is a cumbersome process. This paper proposes a method for building a predictive model for an SICG with different stages based on a recurrent neural network (RNN). In this method, the feed time of a 2- to 10-stage SICG is selected from the standard orthogonal design table as the training and test datasets, and the current filament method (CFM) is used to calculate the dataset label. The gate recurrent unit (GRU) neural network is used to study the training dataset, and the predictive model has good accuracy with respect to the test dataset, with an average error of 0.022. The predictive model and a particle swarm optimization (PSO) algorithm are applied to optimize the feed time of the SICG with different stages. The results show that the three-stage SICG can achieve a muzzle velocity of 50 m/s for a projectile, while the maximum muzzle velocity of the three-stage SICG in all datasets is 46.87 m/s.