Recently, there has been a rapid growth of interest in quadrotors with electric variable-pitch propellers. The control and optimization of such propellers are important factors for improving the flight performance of the vehicles. Therefore, the steady-state identification method to estimate the parameters of the mathematical model of the electric variable-pitch propeller is developed. The steady-state control and optimization scheme with minimum power consumption and the adaptive compensation scheme for the variable-pitch propeller are then proposed, based on which the response performance of the lift force produced by the variable-pitch propeller can be greatly improved by using a cascade compensation scheme. Furthermore, the direct lift-based flight control strategy is presented, which can significantly contribute to the improvement of the flight performance, precisely because the roll, pitch, yaw and vertical channels of the variable-pitch quadrotor are approximately linearized and completely decoupled from each other in this case. The experimental results demonstrate that both the endurance performance and the positioning accuracy of the variable-pitch quadrotor are improved simultaneously by using the proposed method with minimum power consumption.

The aerodynamic parameters of ducted fan micro aerial vehicles (MAVs) are difficult and expensive to precisely measure and are, therefore, not available in most cases. Furthermore, the actuator dynamics with risks of potentially destabilizing the overall system are important but often neglected consideration factors in the control system design of ducted fan MAVs. This paper presents a near-hover adaptive attitude control strategy of a prototype ducted fan MAV with actuator dynamics and without any prior information about the behavior of the MAV. The proposed strategy consists of an online parameter estimation algorithm and an adaptive gain scheduling algorithm, with the former accommodating parametric uncertainties, and the latter approximately eliminating the coupling among axes and guaranteeing the control quality of the MAV. The effectiveness of the proposed strategy is verified numerically and experimentally.

The task of control of unmanned helicopters is rather complicated in the presence of parametric uncertainties and measurement noises. This paper presents an adaptive model feedback control algorithm for an unmanned helicopter stability augmentation system. The proposed algorithm can achieve a guaranteed model reference tracking performance and speed up the convergence rates of adjustable parameters, even when the plant parameters vary rapidly. Moreover, the model feedback strategy in the algorithm further contributes to the improvement in the control quality of the stability augmentation system in the case of low signal to noise ratios, mainly because the model feedback path is noise free. The effectiveness and superiority of the proposed algorithm are demonstrated through a series of tests.

Micro Air Vehicles (MAVs) driven by electric propellers are of interest for military and civilian applications. The rotational speed control of such electric propellers is an important factor for improving the flight performance of the vehicles, such as their positioning accuracy and stability. Therefore, this paper presents a nonlinear adaptive control scheme for the electric propulsion system of a certain MAV, which can not only speed up the convergence rates of adjustable parameters, but can also ensure the overall stability of the adjustable parameters. The significant improvement of the dynamic tracking accuracy of the rotational speed can be easily achieved through the combination of the proposed control algorithm and linear control methods. The experimental test results have also demonstrated the positive effect of the nonlinear adaptive control scheme on the flight performance of the MAV.