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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)

赵旭1,2,齐国元1,蔚昕晨1,胡建兵1,李霞1   

  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

摘要: 风扰、气流扰动和模型未知部分的估计精度直接影响着无人机的稳定性和控制品质,扩张观测器(ESO)能估计这些部分,但存在型别低,收敛性差,估计精度低等问题。补偿函数观测器(CFO)采用纯积分、补偿和传递函数型别的思想,改变了ESO结构,使得CFO较ESO高两个型别,精度高,收敛性强。然而,CFO是利用线性滤波器补偿系统的未知函数或扰动,对快速变化的高次非线性函数补偿能力不足。本文用径向基(RBF)神经网络替代了线性滤波器或积分器,提出了带有RBF神经网络的CFO,进一步提高了估计精度。应用带有RBF神经网络的CFO得到的非线性未知函数和扰动以及微分信息,设计了主动模型函数补偿控制算法,应用Lyapunov稳定性理论证明了闭环系统的稳定性。将该模型补偿控制算法成功地应用于四旋翼飞行器姿态系统的控制。仿真对比了所提出的基于CFO的模型补偿控制,PID控制和自抗扰控制算法,同时在基于Pixhawk的控制测试平台实验中,对比了这三种控制策略,测试四旋翼飞行器对不同参考姿态的跟踪性能。结果表明,所提出的控制方法,在暂态性能和稳态跟踪精度方面,优于其它控制器。

关键词: 四旋翼飞行器, 姿态系统, 补偿函数观测器, 扩张状态观测器, 模型补偿控制, 自抗扰控制

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.

Key words: quadrotor UAV, attitude system, compensation function observer (CFO), extended state observer (ESO), model compensation control (MCC), active disturbance rejection control (ADRC)

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