3/20/2023 0 Comments Model based flight control systemIn addition, there are many other methods that have been proposed, such as those involving a linear quadratic regulator, sliding mode control, and control. In, a robust linear parameter-varying observer was designed, and then, a comparator integrator was used to design the feedback controller. In, a new asymptotic tracking controller was proposed, adopting the robust integral of the signum of the error method and an immersion and invariance-based adaptive control methodology. In, a robust nonlinear controller that combines sliding mode control with backstepping control to improve the antidisturbance ability of the system was proposed. However, PID parameter tuning is difficult and it is sensitive to disturbances. As a classical controller, proportional integral derivative (PID) controllers have been widely used recently. In the past few years, researchers worldwide have proposed many controllers for UAVs. In controller design, it is necessary to simultaneously take into account the amount of algorithm calculation and the control effect. Moreover, a mathematical model of the UAV is difficult to accurately establish, so it is difficult to control a quadrotor UAV for position trajectory tracking. During flight, quadrotors are generally affected by body vibration and external air flow. Quadrotors have the characteristics of nonlinearity, strong coupling, and sensitivity to disturbance. Quadrotors not only play the important role in the military field but also have been widely used in the civil field. Quadrotors have many advantages, such as a simple structure, good flexibility, and vertical take-off and landing. In recent years, quadrotors have gradually become a popular research topic. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. Secondly, the RBF network is used to estimate the unknown parameter of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network.
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