Fractional-order flight control of quadrotor UAS on vision-based precision hovering with larger sampling period

Abstract

Fractional-order flight control has a history of nearly 10 years. Fractional-order controllers (FOCs) have been proved to be better in rising time, overshoot and robustness against plant variation. However, there are still not many real applications of FOC in industry. More case studies need to be carried out to accelerate the maturation of FOC. Quadrotor vision-based control often has a large and time-varying sampling period due to drone’s resource limitation. Therefore, our research has been focused on a specific case for drone vision-based control to investigate the benefits of FOC. In this paper, FOC has been first discovered to be able to tolerate larger sampling period than integer-order controllers. This fact has been proved both theoretically and numerically. First of all, the speed model was identified from real flight tests. Then an integral-order proportional, integral and derivative (IOPID) controller and a fractional-order proportional–derivative (FOPD) controller were designed. After that, a stability criteria, optimization method, graphic method and parallel computing techniques were employed to theoretically prove that the largest sampling period of the designed FOC (0.933 s) is much larger than that of the designed integral-order controller (0.546 s). Later, Simulink simulation with identified linear model proved that the FOC can tolerate larger sampling period. Finally, flight tests showed that the designed FOC has a nearly 20% better precision on drone vision-based hovering than the IOC.