基于樽海鞘群极限学习机的进/发一体化性能寻优控制模型研究

An integrated inlet/engine performance seeking control modelbased on salp swarm algorithm extreme learning machine

为充分发挥航空推进系统的性能,提高性能寻优控制的实时性,将樽海鞘群算法(SSA)与极限学习机(ELM)相结合,基于进/发一体化部件级模型建立数据集,提出一种基于SSA-ELM的数据驱动模型。将该建模方法与广义回归神经网络(GRNN)、BP神经网络(BPNN)和极限学习机(ELM)比较,结果表明,相比于BPNN,ELM,GRNN,SSA-ELM用于预测可以使安装推力的均方根误差(RMSE)分别降低7.41%,17.01%,72.57%,安装油耗的RMSE分别降低4.32%,19.41%,66.77%,具有更高的预测精度。将基于SSA-ELM的数据驱动模型作为机载模型应用到性能寻优控制,结果表明,该机载模型能够维持理想的寻优效果。针对最大安装推力模式开展实时性分析,该机载模型相比于进/发一体化部件级模型,平均计算时间由184.05 ms缩短至1.357 ms,实时性得到显著改善,大大提高了寻优效率。

To fully exploit the performance of the aero-propulsion system and improve the real-time perfor⁃mance of performance seeking control,a data-driven model based on SSA-ELM is proposed by combining Salp Swarm Algorithm(SSA)and Extreme Learning Machine(ELM)and establishing data sets based on integrated in⁃let/engine component-level model.The modeling method was compared with General Regression Neural Network(GRNN),BP Neural Network(BPNN)and Extreme Learning Machine(ELM).The results show that compared with BPNN,ELM and GRNN,SSA-ELM for prediction can reduce the Root Mean Square Error(RMSE)of in⁃stalled thrust by 7.41%,17.01%,and 72.57%,respectively,and the RMSE of installed fuel consumption by 4.32%,19.41%,and 66.77%,respectively,which has higher prediction accuracy.The data-driven model based on SSA-ELM was applied to the performance seeking control as on-board model.The results show that the on-board model can maintain the ideal seeking effect.In the real-time performance analysis of the maximum in⁃stalled thrust mode,the average computation time of the on-board model is reduced from 184.05 ms to 1.357 ms compared to the integrated inlet/engine component-level model,which significantly improves the real-time per⁃formance and greatly enhances the seeking efficiency.