Cooperative Guidance of Multiple Missiles: A Hybrid Coevolutionary Approach

Cooperative guidance of multiple missiles is a challenging task with rigorous constraints of time and space consensus, especially when attacking dynamic targets. In this article, the cooperative guidance task is described as a distributed multiobjective cooperative optimization problem. To address t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on control systems technology 2024-01, Vol.32 (1), p.128-142
Main Authors: Lan, Xuejing, Chen, Junda, Zhao, Zhijia, Zou, Tao
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Cooperative control
Cooperative guidance
evolutionary strategy (ES)
Guidance (motion)
Missile guidance
Missiles
Multi-agent systems
multiobjective optimization
Multiple objective analysis
Navigation
Optimal control
Optimization
Topology
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cooperative guidance of multiple missiles is a challenging task with rigorous constraints of time and space consensus, especially when attacking dynamic targets. In this article, the cooperative guidance task is described as a distributed multiobjective cooperative optimization problem. To address the issues of nonstationarity and continuous control faced by cooperative guidance, the natural evolutionary strategy (NES) is improved along with an elitist adaptive learning technique to develop a novel natural coevolutionary strategy (NCES). The gradients of the original evolutionary strategy (ES) are rescaled to reduce the estimation bias caused by the interaction between the multiple missiles. A hybrid coevolutionary cooperative guidance law (HCCGL) is then developed by integrating the highly scalable co-ES and the proportional guidance law, with detailed convergence proof provided. Finally, simulations demonstrated the effectiveness and superiority of this guidance law in solving cooperative guidance tasks with high accuracy, with potential applications in other multiobjective optimization, dynamic optimization, and distributed control scenarios.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2023.3301141