Cooperative Guidance of Multiple Missiles: A Hybrid Coevolutionary Approach
Xuejing Lan, Junda Chen, Zhijia Zhao, Tao Zou
Abstract
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.