Abstract
In order to improve the autonomy of gliding guidance for complex flight missions, this paper proposes a multiconstrained intelligent gliding guidance strategy based on optimal guidance and reinforcement learning (RL). Three-dimensional optimal guidance is introduced to meet the terminal latitude, longitude, altitude, and flight-path-angle constraints. A velocity control strategy through lateral sinusoidal maneuver is proposed, and an analytical terminal velocity prediction method considering maneuvering flight is studied. Aiming at the problem that the maneuvering amplitude in velocity control cannot be determined offline, an intelligent parameter adjustment method based on RL is studied. This method considers parameter determination as a Markov Decision Process (MDP) and designs a state space via terminal speed and an action space with maneuvering amplitude. In addition, it constructs a reward function that integrates terminal velocity error and gliding guidance tasks and uses Q-Learning to achieve the online intelligent adjustment of maneuvering amplitude. The simulation results show that the intelligent gliding guidance method can meet various terminal constraints with high accuracy and can improve the autonomous decision-making ability under complex tasks effectively.
Highlights
Hypersonic gliding vehicles have become the focus and hotspot of the aerospace industry due to their long-range flight and large-scale maneuverability
Gliding guidance faces challenges such as complex flight environment, strong uncertainty, diversified flight missions, multiple processes, and terminal constraints [1]. erefore, gliding guidance methods need to ensure the accuracy of terminal constraints, the robustness of process deviations, and the adaptability of diverse guidance tasks
The standard trajectory tracking is the most traditional gliding guidance method. is method can be divided into two parts: first, the standard trajectory design that meets a variety of process constraints and terminal constraints, and second, the guidance command calculation to ensure guidance accuracy and robustness, that is, trajectory tracking [2]. is method has strong reliability and can reduce the online cost of calculation, but the standard trajectory and tracking control parameters need to be redesigned when guidance mission is changed, which limits the adaptability [3]
Summary
Hypersonic gliding vehicles have become the focus and hotspot of the aerospace industry due to their long-range flight and large-scale maneuverability. Erefore, gliding guidance methods need to ensure the accuracy of terminal constraints, the robustness of process deviations, and the adaptability of diverse guidance tasks. Is method can be divided into two parts: first, the standard trajectory design that meets a variety of process constraints and terminal constraints, and second, the guidance command calculation to ensure guidance accuracy and robustness, that is, trajectory tracking [2]. Is method has strong reliability and can reduce the online cost of calculation, but the standard trajectory and tracking control parameters need to be redesigned when guidance mission is changed, which limits the adaptability [3]. The optimal gliding guidance method is used to meet the terminal latitude, longitude, altitude, and flight-path-angle (FPA) constraints. A framework model of RL is established, and Q-learning is used to adjust the maneuvering amplitude intelligently in velocity control to ensure the terminal velocity control accuracy. is guidance strategy will solve the problem of “dimensional disaster” and guarantee learning efficiency and realize multiconstrained adaptive guidance
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