Design of thrust estimator in the solid rocket ramjet based on PSO-BP neural network

A solid rocket ramjet thrust estimation method based on BP（back propagation）neural network optimized by PSO（particle swarm optimization）was proposed for direct control of the thrust of solid rocket ramjet. In detail，SPSO（standard PSO）and three different BBPSO （bare bones PSO）methods were adopted to optimize the network weights. Then， the optimal weights as the initial value were tuned finely by BP neural network training. Therefore，the non‑ linear relationship between thrust and gas flow， flight Mach number as well as flight height was obtained such that the design of thrust estimator was completed. 240 sets of training data were used to train the network，and 180 sets of testing data were used to verify the network. The simu‑ lation results showed that among four different PSO methods such as SPSO，BBExp（exploiting BBPSO）， ABPSO* （modified adaptive BBPSO） and SNPSO （simplified pruning strategy based BBPSO）， the design of thrust estimator based on BP neural network optimized by SNPSO is the most convenient and effective method not only due to its simple form but also due to its capacity of controlling the relative thrust error within 5% for test set data. © 2022 BUAA Press. All rights reserved.