A deep reinforcement learning-based approach to onboard trajectory generation for hypersonic vehicles

An onboard three-dimensional (3D) trajectory generation approach based on the reinforcement learning (RL) algorithm and deep neural network (DNN) is proposed for hypersonic vehicles in glide phase. Multiple trajectory samples are generated offline through the convex optimisation method. The deep lea...

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Bibliographic Details
Published in:Aeronautical journal 2023-09, Vol.127 (1315), p.1638-1658
Main Authors: Bao, C.Y., Zhou, X., Wang, P., He, R.Z., Tang, G.J.
Format: Article
Language:English
Subjects:
Algorithms
Altitude
Artificial intelligence
Artificial neural networks
Convexity
Deep learning
Hypersonic vehicles
Machine learning
Neural networks
Onboard
Optimal control
Real time
Trajectories
Vehicles
Velocity
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Summary:An onboard three-dimensional (3D) trajectory generation approach based on the reinforcement learning (RL) algorithm and deep neural network (DNN) is proposed for hypersonic vehicles in glide phase. Multiple trajectory samples are generated offline through the convex optimisation method. The deep learning (DL) is employed to pre-train the DNN for initialising the actor network and accelerating the RL process. Based on the offline deep policy deterministic actor-critic algorithm, a flight target-oriented reward function with path constraints is designed. The actor network is optimised by the end-to-end RL and policy gradients of the critic network until the reward function converges to the maximum. The actor network is considered as the onboard trajectory generator to compute optimal control values online based on the real-time motion states. The simulation results show that the single-step online planning time meets the real-time requirements of onboard trajectory generation. The significant improvement in terminal accuracy of the online trajectory and the better generalisation under biased initial states for hypersonic vehicles in glide phase is observed.
ISSN:0001-9240
2059-6464
DOI:10.1017/aer.2023.4