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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.

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.

The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simulations revealed that the dynamic performance and robustness of the ADRC is better than that of the SMC.