@article{DENG2026, 
author = {Yi DENG and Liguo SUN and Yonghao PAN and Jiayi YAN and Yuanji LIU},
title = {Reinforcement learning based intelligent fault-tolerant assistance control for air-breathing hypersonic vehicles},
year = {2026},
journal = {Chinese Journal of Aeronautics},
volume = {39},
number = {3},
keywords = {Hypersonic vehicles, Fault-tolerant control, Reinforcement learning, Heuristic programming, Online learning},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103708},
doi = {10.1016/j.cja.2025.103708},
abstract = {This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles (AHVs). Considering that Reinforcement Learning (RL) has the advantage of exploring approximate optimal strategies, an RL-based assistance controller parallel to the fundamental controller is introduced to generate the assistance control signal. Specifically, the Incremental model-based Dual Heuristic Programming (IDHP) method is adopted to design the RL-based assistance control law. In order to extend the IDHP method to the assistance control scenario, a novel linear time-varying incremental model of the closed-loop augmented system is constructed and identified in real time, which consists of the AHV plant, the fundamental controller, and the command generator. The RL agent continuously updates its neural-network weights according to the real-time identification information, and adjusts its control policy, i.e., the assistance control signal, after detecting sudden model changes. Simulation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/configuration parameter uncertainties. The fault-tolerant ability of the whole control system with the proposed RL-based assistance controller is validated in both inner-loop attitude and outer-loop altitude tracking tasks.}
}