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 present paper addresses an attitude tracking control problem of a ducted fan microaerial vehicle. The proposed indirect adaptive controller can greatly reduce tracking error in the initial stage of the adaptive learning process by using an error compensation strategy and can achieve good capability to eliminate the adverse effect of measurement noises on the convergence of adjustable parameters. Moreover, the learning rate adaptation strategy is proposed to further minimize the adverse effect of large learning rates on the convergence of adjustable parameters. The experimental tests have illustrated the effectiveness of the proposed adaptive controller.

Ducted fan UAV (Unmanned Aerial Vehicle) has broad application prospective. To study flight dynamics and flight control problems of ducted fan UAV, this paper developed a refined aerodynamic model for the ducted fan UAV by wind tunnel test. Based on that, the flight dynamic model of ducted fan UAV was built, which showed features as being nonlinear and unstable and of strong coupling and, therefore, inspired this paper to adopt a strong adaptive control approach based on decoupling to perform attitude control on the ducted fan vehicle. The approach combined model tracking control and reference model adaptive control together, which highly reduced attitude tracking error and improved stability margin of the closed-loop system, and was successfully applied in flight control simulation and flight tests of the vehicle.

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.