### 补偿函数观测器及其在飞行器姿态控制中应用(稿号22-27108退稿重投)

1. 1. 天津工业大学
2. 天津工业大学控制科学与工程学院
• 收稿日期:2022-03-31 修回日期:2022-06-03 出版日期:2022-06-08 发布日期:2022-06-08
• 通讯作者: 齐国元
• 基金资助:
国家自然科学基金项目

### Compensation Function Observer and its Application in UAV Attitude Control（Manuscript No.22-27108 Rejected and resubmitted）

• Received:2022-03-31 Revised:2022-06-03 Online:2022-06-08 Published:2022-06-08

Abstract: The estimate accuracy of wind disturbance, airflow disturbance and unknown part of the model directly affects the stability and control performance of the UAV. The extended observer (ESO) can estimate these parts, but there are problems of low type and low estimate accuracy. Compensation Function Observer (CFO) adopts the idea of pure integral, compensation and transfer function type, and changes the structure of ESO, making CFO two types higher than ESO, with high precision and strong convergence. However, CFO uses a linear filter to compensate the un-known function or disturbance of the system, and has insufficient compensation ability for fast-changing or high-order nonlinear functions. Because the unknown model function is often a nonlinear function, in this paper, a radial basis (RBF) neural network is used to replace the linear filter, and a compensation function observer with RBF neu-ral network is proposed, which further improves the estimation accuracy. Using the nonlinear unknown model and disturbance and differential information obtained by the compensation function observer with RBF neural network, an active model compensation control algorithm is designed and successfully applied to the control of the quadrotor UAV attitude system. The stability of the closed-loop system is proved by applying Lyapunov stability theory. Through simulations, the proposed model compensation control based on the compensation function observer is compared with the PID control and active disturbance rejection control algorithm. On the Pixhawk-based control test platform experiment, the quadrotor UAV is tested using these three control strategies for different Tracking performance for reference poses. The results show that the proposed control method substantially outperforms other controllers in transient performance and steady-state tracking accuracy.