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.

Tiltrotors have three flight modes that pose control problems and quality defects during the conversion process. To address this, a novel flying qualities-based time-varying stability augmentation system is designed to achieve multi-mode, nonlinear, and time-varying stability. The system integrates a nonlinear time-varying control law with the flying qualities requirements for all three flight modes. It consists of an inner and outer loop control framework, where the control law in the inner loop is designed based on the Lyapunov theorem of stability. The reference models in the outer loop are derived from the flying qualities criteria to meet level one flying qualities requirements. To evaluate the conversion process, a time-varying flying qualities evaluation method is developed, which includes the conversion path, pilot model, and time-varying flying qualities index. The proposed time-varying stability augmentation control system is then tested through simulation during the conversion process. A pilot-aircraft closed-loop system is established for conducting experiments. Comparison between simulation results and pilot-in-loop experiment results demonstrates the effectiveness of the proposed control system. Furthermore, it proves that the evaluation method is suitable for analyzing time-varying systems. This research can be valuable in designing and evaluating stability augmentation controls for strongly time-varying systems.