Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V still remain to be investigated, one of which is automatic flight control. Due to the unconventional airframe shape of the Flying V, aerodynamic modelling cannot rely on validated aerodynamic-modelling tools and the accuracy of the aerodynamic model is uncertain. Therefore, this contribution investigates how an automatic flight controller that is robust to aerodynamic-model uncertainty can be developed, by utilising Twin-Delayed Deep Deterministic Policy Gradient (TD3) - a recent deep-reinforcement-learning algorithm. The results show that an offline-trained single-loop altitude controller that is fully based on TD3 can track a given altitude-reference signal and is robust to aerodynamic-model uncertainty of more than 25%.
|Title of host publication||AIAA SciTech Forum 2023|
|Number of pages||19|
|Publication status||Published - 2023|
|Event||AIAA SCITECH 2023 Forum - National Harbor, MD & Online, Washington, United States|
Duration: 23 Jan 2023 → 27 Jan 2023
|Conference||AIAA SCITECH 2023 Forum|
|Period||23/01/23 → 27/01/23|