利用扩展卡尔曼滤波对无人机进行在线辨识
Research of online identification of UAV using extended Kalman filter
郭永帅1
作者信息
- 1. 安徽公安职业学院网安系,安徽合肥 230031
- 折叠
摘要
为了实施带自适应增益的PID控制,较好地实现轨迹跟踪任务,提出一种基于扩展卡尔曼滤波的在线辨识方法.首先,对六旋翼无人机(UAV)以离散时间模型进行建模,考虑了 UAV控制系统的非线性特征.然后,针对UAV非线性离散时间模型,开发基于扩展卡尔曼滤波器的神经估计器,利用状态估计完成在线辨识,从而调节PID增益,完成跟踪任务.实验结果验证了所提方法的有效性,所提辨识模型可应用于非线性和未知动力学的系统,具有一定实用性.
Abstract
In order to implement PID control with adaptive gain and better track tracking task,an online identification method based on extended Kalman filter is proposed.Firstly,a discrete-time model of six rotor UAV is established,and the nonlinear characteristics of UAV control system are considered.Then,according to the nonlinear discrete-time model of UAV,a neural estimator based on extended Kalman filter is developed.The state estimation is used to complete on-line identification,so that the PID gain can be adjusted to complete the tracking task.Experimental results verify the effectiveness of the proposed method.The proposed identification model can be applied to nonlinear and unknown dynamic systems,and has certain practicability.
关键词
PID控制/扩展卡尔曼滤波/无人机/在线辨识/非线性Key words
PID control/extended Kalman filter/UAV/online identification/nonlinear引用本文复制引用
基金项目
安徽省2022年度高等学校省级质量工程项目(2022cjrh007)
出版年
2024
NETL
NSTL
万方数据