Rapid optimal control law generation: an MoE based method  

作　　者：ZHANG Tengfei SU Hua GONG Chunlin YANG Sizhi BAI Shaobo 

机构地区：[1]Shaanxi Aerospace Flight Vehicle Design Key Laboratory,School of Astronautics,Northwestern Polytechnical University,Xi’an 710072,China [2]Northwest Industries Group Company,Xi’an 710043,China [3]Northwest Institute of Mechanical and Electrical Engineering,Xianyang 712099,China

出　　处：《Journal of Systems Engineering and Electronics》2025年第1期280-291,共12页系统工程与电子技术(英文版)

基　　金：Defense Industrial Technology Development Program (JCKY2020204B016);National Natural Science Foundation of China (92471206)。

摘　　要：To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.

关 键 词：optimal control mixture of experts(MoE) K-MEANS Kriging model neural network classification principal component analysis(PCA) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TJ765[自动化与计算机技术—控制科学与工程]