Research On Attitude Calculation And Anti-disturbance Control Of Quadrotor UAV

With the development of technology quadrotor drones are already in use in a large number of industries.However,its attitude control system is susceptible to external wind disturbances and unknown disturbances,and most quadcopters choose cheap and less accurate sensors,and the attitude information obtained by these sensors contains a lot of noise.Therefore,this paper focuses on the attitude solving algorithm and attitude control algorithm to improve the stability and accuracy of the quadrotor UAV attitude control system.Firstly,by studying the basic construction characteristics and flight characteristics of quadrotor UAV,the mathematical model of its nonlinear system is constructed.Secondly,for the problem of noise interference in the attitude information acquired by the airborne sensors,the characteristics of each airborne sensor are analyzed,the complementary filtering algorithm based on quaternion and the extended Kalman filtering algorithm based on quaternion are studied.The simulation results in both flight and stationary states show that the quaternion-based EKF algorithm has a better noise suppression effect.Next,for wind disturbance and unknown disturbance causing uncertainty in attitude control system model.Using the back propagation property,an attitude controller capable of handling nonlinear problems is designed.However,MATLAB/Simulink simulations show that wind perturbations exist and that the back-propagation control is not resistant to wind perturbations.Therefore,sliding mode variable structure control and adaptive control are added to the back propagation control method.The simulation results show that the wind resistance of the system is improved by the sliding mode control and adaptive control.However,the antidisturbance performance under unknown disturbances is poor and the adaptive control method cannot satisfy the unpredictable linear dynamic problem.Considering that RBF neural networks can approximate unknown nonlinear functions with arbitrary accuracy,a quadrotor UAV adaptive backstepping sliding mode attitude controller based on RBF neural networks is proposed,and the particle swarm optimization algorithm is used to optimize each parameter value to improve the accuracy and robustness of the controller to improve the accuracy.Demonstrate that the attitude controller meets the stability requirements by using Lyapunov’s stability theorem.MATLAB/Simulink simulation results show that the adaptive back propulsive sliding mode control system of quadrotor UAV based on RBF network has good antiinterference ability in suppressing wind interference and unknown interference.Finally,in order to further verify the rationality of the designed attitude controller,a quadrotor UAV experimental platform was built using the available accessories,the program structure of the APM flight control was analyzed,the RBF neural network adaptive back propagation sliding mode control algorithm was programmed into the control library by Notepad+ and Arduino software.Hovering flight tests were conducted under working conditions,the experimental results show that the quadrotor UAV adaptive inverse sliding mode control device based on RBF neural network has good anti-interference performance.