基于自适应RBF神经网络具有模型不确定性的四旋翼无人机指定时间预设性能控制方法OA北大核心CSTPCD

Quadrotor UAVs are characterized by strong coupling and underdrive,and are easily affected by external interference during flight,which in turn affects the stability and accuracy of the whole UAV system.Aiming at this problem,a specified-time preset performance constraint control policy based on RBF neural network was proposed.Firstly,in view of the difficulty of establishing an accurate mathematical model for the uncertain mathematical model of the quadrotor UAV and the existence of external unknown disturbances during the execution of the mission,a control method based on the specified time preset performance constraints was proposed,and the trajectory tracking problem of the quadrotor UAV was transformed into the desired command tracking problem for the position subsystem and the attitude subsystem;in view of the design of the controller,in order to solve the problem of the"position subsystem",the RBF neural network was used to design the controller.Secondly,a compensation system was introduced to solve the filter error caused by the"differential explosion"problem during the controller design process.Finally,the unknown external perturbations were compensated by RBF neural network approximation and the predicted results were compensated to the controller to improve the robustness.Finally,the simulation method is used to verify the stability and performance advantages of UAV control system,flight tests were conducted to verify that the actual flight trajectory in a breeze gathering environment tended to be consistent with the simulation results.The deviation of the autonomous trajectory tracking takeoff and landing position was less than 1 cm,demonstrating the effectiveness of the proposed algorithm.