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In order to overcome the influence of internal and external disturbances caused by rotor tilt motion and gust disturbance on the full flight mode control of a tilt-rotor unmanned aerial vehicle (UAV), a design method using fuzzy backstepping control based on an extended-state observer (FBS-ESO) is proposed. In this paper, fuzzy control is used to tune the parameters of the backstepping control law online, and the extended-state observer estimates the total disturbance of the controlled system to improve the controller’s robustness and anti-disturbance capability. This paper designs the attitude control system of a tilt-rotor UAV based on an FBS-ESO controller. The control performance of the FBS-ESO controller is tested in a hardware-in-loop simulation of the attitude control system. The simulation results show that changing the rotor tilt angle will destroy the stability of the traditional backstepping controller and active disturbance rejection controller (ADRC). In contrast, the FBS-ESO controller maintains good control performance. In addition, the performance of the FBS-ESO controller is not be significantly affected by adding external gust disturbance or changing the UAV parameters in the simulation. These disturbances significantly impact the traditional backstepping controller and ADRC. Therefore, the FBS-ESO controller has better anti-disturbance capabilities and robustness, as well as higher attitude control accuracy.

To solve the problem that the trajectory tracking control system of the unmanned helicopter is limited by the accuracy of the physical model (parameters and dynamic characteristics) and external disturbance of the unmanned helicopter, this paper designs a trajectory tracking control system of the unmanned helicopter based on the linear/nonlinear hybrid Active Disturbance Rejection Control (ADRC). At the same time, this paper proposes a method for tuning the controller parameters based on the Bacterial Foraging Optimization-Flower Pollination Algorithm (BFO-FPA). Finally, the simulation test of spiral climb and “8”-figure climb is applied to verify the controller’s performance. The results show that the controller proposed in this paper can effectively overcome the influence of the unmanned helicopter’s internal and external disturbance, and the controller has the advantages of strong anti- disturbance ability and strong robustness. Simultaneously, the optimization algorithm can obtain the optimal global solution, and can improve the controller’s performance.

To enable the unmanned helicopter to fly autonomously in precise paths and reduce the influence of internal and external unknown disturbances of the unmanned helicopter, this paper proposes the adaptive radial basis function (RBF) neural network-based active disturbance rejection controller (ADRC). This controller is abbreviated as RBF-ADRC. Firstly, this paper introduces the flight dynamics model of the unmanned helicopter and the control features of the traditional ADRC. Subsequently, this paper uses modern control theory to establish a state observer and uses adaptive RBF neural network to estimate the unknown total disturbance. Finally, this paper constructs the unmanned helicopter's trajectory tracking control system based on the RBF-ADRC controller. The simulation results of the spiral ascent and the “8”-figure climb maneuver flight prove that the anti-disturbance, robustness and tracking accuracy of the RBF-ADRC are better than the traditional ADRC and proportion-integration-differentiation (PID) control methods.