关键词: 高超声速飞行器, 制导控制一体化, 深度强化学习, 自适应参数, 反步控制

Abstract: Aiming at the dynamic optimization problem of guidance and control parameters of hypersonic flight vehicles, a Deep reinforcement learning parameter tuning method based on the twin delayed deep deterministic policy gradient (TD3) algorithm is proposed. Firstly, the motion model of the hypersonic flight vehicle and the integrated model of guidance and control were established, and the controller based on the backstepping method was designed. The consistent final boundedness was proved through Lyapunov stability. Then, the controller parameter optimization problem was transformed into a Markov decision process model, and the data-driven online adaptive optimization of controller parameters was achieved based on the TD3 algorithm. This method constructs a parameter optimization mechanism that integrates the prior knowledge of the model and data-driven approaches, significantly enhancing the autonomous adaptability of the controller in the parameter space. Finally, the effectiveness and robustness of the proposed method were verified through numerical simulation.

Key words: hypersonic flight vehicle, integrated guidance and control, deep reinforcement learning, adaptive parameters, backstepping control