Mirtaba, MohammadOveissi, ParhamGoel, Ankit2024-11-142024-11-142024-10-04https://doi.org/10.48550/arXiv.2410.03815http://hdl.handle.net/11603/36948This paper demonstrates the sim-to-real capabilities of retrospective cost optimization-based adaptive control for multirotor stabilization and trajectory-tracking problems. First, a continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm is developed. Next, a computationally inexpensive 12-degree-of-freedom model of a multirotor is used to learn the control system in a simulation environment with a single trajectory. Finally, the performance of the learned controller is verified in a complex and realistic multirotor model in simulation and with a physical quadcopter in a waypoint command and a helical trajectory command.7 pagesen-USAttribution-NonCommercial-ShareAlike 4.0 International CC BY-NC-SA 4.0 Deedhttps://creativecommons.org/licenses/by-nc-sa/4.0/Computer Science - Systems and ControlUMBC Estimation, Control, and Learning Laboratory (ECLL).Electrical Engineering and Systems Science - Systems and ControlText