  The realization of motion description is a challenging work for fixed-wing
Unmanned Aerial Vehicle (UAV) acrobatic flight, due to the inherent coupling
problem in ranslational-rotational motion. This paper aims to develop a novel
maneuver description method through the idea of imitation learning, and there
are two main contributions of our work: 1) A dual quaternion based dynamic
motion primitives (DQ-DMP) is proposed and the state equations of the position
and attitude can be combined without loss of accuracy. 2) An online
hardware-inthe-loop (HITL) training system is established. Based on the DQDMP
method, the geometric features of the demonstrated maneuver can be obtained in
real-time, and the stability of the DQ-DMP is theoretically proved. The
simulation results illustrate the superiority of the proposed method compared
to the traditional position/attitude decoupling method.
