发表刊物：CJA

刊物所在地：中国

关键字：This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles (AHVs). Considering that Reinforcement Learn-ing (RL) has the advantage of exploring approximate optimal strategies, an RL-based assistance controller parallel to the fundamental controller isintroduced to generate the assistance controlsignal. Speciffcally, 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 identiffed 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 identiffcation information, and adjustsits control policy, i.e., the assistance controlsignal, after detecting sudden model changes. Simu-lation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/conffguration 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

合写作者：潘永豪

第一作者：邓毅（学生）

论文类型：期刊论文

通讯作者：孙立国

是否译文：否

发表时间：2025-07-01

收录刊物：SCI