Enhanced Intelligent Fault-Tolerant Control for Hypersonic Gliding Vehicles: Combing DRL and Transfer Learning

To improve the fault-tolerant adaptability of hypersonic gliding vehicles (HGVs) in diverse environments and actuator faults, an enhanced intelligent fault-tolerant control (FTC) scheme is proposed based on the deep reinforcement learning (DRL) and transfer learning (TL) method. First, the HGV model with multiple disturbances and diverse actuator faults is constructed. On this basis, a fundamental FTC system is designed under the active disturbance rejection control (ADRC) framework to provide theoretical stability and basic FTC capability. Then, a DRL-based intelligent FTC scheme is proposed to autonomously adapt the FTC parameters to different environments and faults. Furthermore, to address the discrepancies between the actual flight environments and virtual training environments, as well as the issue that the actual faults exceed the knowledge of the FTC agent, a TL-based enhanced intelligent FTC scheme is proposed to ensure that the FTC agent can continuously update its policy using actual flight data, thereby improving its adaptability to more complex and unknown conditions.