一种基于学习的高超声速飞行器智能控制方法

Aiming at the flight control problem of air-breathing hypersonic vehicle, an intelligent control method based on learning is proposed. In order to facilitate the controller design, the vehicle dynamics model is divided into velocity subsystem and altitude subsystem. For the constrained-input problem in the velocity subsystem, an intelligent control scheme based on reinforcement learning is proposed. For the altitude control problem with limited communication resources, an event-triggered deterministic learning control scheme is proposed. The method includes two stages: offline learning and online triggered control. Firstly, the unknown dynamic knowledge of the system is acquired and stored in the local offline learning phase, and then an online event-triggered controller is designed using the acquired empirical knowledge. Based on the idea of learning, the agent and experience knowledge acquired by offline learning are applied to online control, so that the proposed method can quickly calculate control commands and occupy less communication resources. The effectiveness of the proposed method is verified by simulation results.