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.

This paper focuses on the decentralized control problem of multi-agent systems with multiple control objectives including target tracking, velocity synchronization, collision avoidance, and obstacle avoidance. To enable the real-time application and optimization, a control scheme is constructed based on the idea of adaptive dynamic programming (ADP). The cost function is defined according to the control objectives and approximated by a critic neural network. An action neural network is applied to approximate the optimal controller, which is achieved by minimizing the cost function. Based on the deduced weight updating laws, the proposed control scheme can learn online with good adaptability to unknown environment. Finally, a simulation of seven agents moving in two-obstacle environment is conducted to show the validity of the intelligent controller.