摘要
为了提高无人机(UAV)姿态解算的精度和稳定性,提出了一种基于粒子群优化(PSO)的级联互补滤波(CCF)的姿态解算方法——PCCF。该方法设计了一种新颖的非线性互补滤波器和线性滤波器级联的滤波结构,前者用于校正陀螺仪零偏,后者用于估计姿态角。采用PSO算法自适应地计算级联互补滤波结构的增益参数,避免人工调节或者基于经验的方法容易陷入局部最优解的问题。实验结果表明,相对于传统滤波方法,本文所提出的姿态解算方法能够获得更为精确、可靠的姿态信息,静态时姿态估计误差小于0.1°,动态时姿态估计误差范围为±1°,满足无人机精准导航和稳定飞行对姿态解算的要求。
To enhance the precision and stability of unmanned aerial vehicle(UAV)attitude solution,a particle swarm optimization(PSO)-based cascaded complementary filtering(PCCF)attitude solution method is proposed.This method designs a novel structure of cascaded filtering,consisting of a nonlinear complementary filter for gyroscope bias correction and a linear filter for attitude angle estimation.The PSO algorithm is employed to adaptively compute the gain parameters of the cascaded complementary filtering structure,avoiding the issues of manual adjustment or empirical methods prone to fall into local optimal solution.Experimental results demonstrate that compared to traditional filtering methods,the proposed method can obtain more accurate and reliable attitude information,the static attitude estimation error is less than 0.1°,and the dynamic attitude estimation error range is±1°,meeting the requirements of precise navigation and stable flight for UAV.
作者
李园园
朱书慧
LI Yuanyuan;ZHU Shuhui(School of Information Engineering,Zhengzhou Shengda University,Zhengzhou 451191,China;Faculty of Education,Henan University,Kaifeng 475001,China)
出处
《传感器与微系统》
CSCD
北大核心
2024年第8期25-29,共5页
Transducer and Microsystem Technologies
基金
2022年度河南省教师教育课程改革研究项目(2022-JSJYYB-017)
2022年度河南省高等学校重点科研项目计划项目(22A880004)
2021年郑州升达经贸管理学院教育教学改革研究项目(SDJG-2021-YBZ40)。
关键词
级联互补滤波
粒子群优化算法
姿态解算
无人机
惯性测量单元
cascaded complementary filtering
particle swarm optimization(PSO)
attitude solution
unmanned aerial vehicle(UAV)
inertial measurement unit(IMU)
作者简介
李园园(1981-),女,硕士,讲师,主要从事无人飞行器控制技术方面的研究工作;朱书慧(1986-),女,博士,副教授,主要从事多传感器融合技术方面的研究工作。
