Attitude Controller Design For Ducted Fan UAVs At Hovering State

Ducted fan UAVs are neoteric aircrafts with the ability of vertical takeoff and landing, hovering and forward flying, which have a broad application prospect. Hovering state is the major condition of this kind of UAVs and the premise of forward flying. It is valuable to design attitude controller for the hovering state of ducted fan UAVs to solve the problem of poor static-stability. The model reference adaptive controller is introduced to effectively suppress the influence of uncertainty parameters of the attitude model and achieve good attitude control effect.The major work of this article is to design attitude controller for ducted fan UAVs at hovering state. Firstly, the control method statuses of ducted fan UAVs are analyzed and model reference adaptive control algorithm is chosen to design attitude controller for ducted fan UAVs. Secondly, the hovering state of ducted fan UAVs is modeled, and the linear model is acquired by simplification, and the uncertainty of model is analyzed quantitatively, showing that the parameter uncertainty is the main uncertainty. Thirdly, PID attitude controller and model reference adaptive attitude controller are designed for ducted fan UAVs separately, and the problems of steering nonlinear saturation of model reference adaptive controller and parameter uncertainty impact of decoupling controller are solved. Finally, the attitude control effect of PID controller and the model reference adaptive controller are compared when the parameter uncertainty takes boundary value. The biggest parameter uncertainty ranges allowed by PID controller and model reference adaptive controller are compared.According to analytical result of the work, we can make some conclusions: 1) the model reference adaptive controller is more adaptive than the PID controller when the parameter uncertainty takes boundary value, the control effect of the PID controller will be constantly affected by the parameter uncertainty, while the model reference adaptive controller makes the performance of the system match the ideal design quickly, and the impact of parameter uncertainty is soon suppressed; 2) model reference adaptive controller is more robust and allows larger parameter uncertainty of model than the PID controller, the performance of the system can satisfy the design requirements for a larger parameter uncertainty range.