风干扰下无人机自抗扰控制参数自整定

Self-tuning Parameters of Active Disturbance Rejection Control for UAV under Wind Disturbance

针对室外环境中的复杂气流扰动对无人机姿态控制的影响,对姿态控制结构和控制器参数自整定进行研究。首先,建立复杂风场环境模型,并将其引入到无人机动力学模型中;其次,在此基础上,设计自抗扰控制器,并针对自抗扰控制参数过多整定困难的问题,使用RBF神经网络实现控制器参数自整定,采用IAE和ITAE性能指标来判断控制器性能好坏。通过与常规PID和反步法进行对比,证明了基于RBF神经网络优化后的非线性自抗扰控制器具有较高的控制精度和较好的抗风扰能力。

Aiming at the influence of complex airflow disturbance in outdoor environment on UAV attitude control,the optimization of attitude control structure and controller parameters is studied.The complex wind field environment model is established,and it is introduced into the dynamics model of UAV.Then,on the basis of this,an active disturbance rejection controller(ADRC)is designed,and a self-tuning method of controller parameters based on RBF neural network algorithm is proposed to solve the problem that too many ADRC parameters lead to difficulty in design.The IAE and ITAE performance indexes are used to judge the transient performance and steady-state performance of the controller,and it is proved that the nonlinear active disturbance rejection controller based on RBF neural network has higher control accuracy and better wind disturbance resistance ability.