风场环境下四旋翼飞行器抗干扰研究
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摘要
针对四旋翼在室外飞行时易受到气流干扰,难以实现精准控制的问题,首先对四旋翼在室外飞行时的风场环境进行建模,将风场影响添加到四旋翼动力模型当中;其次,设计了自适应扩展卡尔曼滤波器(Adaptive extended kalman filter,AEKF),通过实时调整噪声协方差的自适应因子提高飞行器姿态数据的滤波精度,并将数据反馈给PID位置控制器对飞行器进行控制。实验表明,建立的模型能够有效反映四旋翼在风场环境下的运动规律,采用PID与AEKF相结合的控制策略可以提高系统的抗干扰能力,实现在风场环境下对四旋翼的精准控制。
Aiming at the problem that the quad-rotor unmanned aerial vehicles(UAV) is vulnerable to be interfered by airflow when flying outside, and it is difficult to achieve accurate control. Firstly, the turbulence of the quad-rotor UAV during outdoor flight is modeled; and the influence of the turbulence is added to the UAV dynamic model. Then, the adaptive extended Kalman filter is designed to improve the filtering accuracy of the UAV attitude data by adjusting the adaptive factor of the noise covariance in real time, and the data is feedback to the PID position controller to control the UAV. The experimental results show that the established model can effectively reflect the motion law of the quadrotor UAV in the wind field environment, and the combination of the PID and AEKF can improve the anti-interference ability of the system and realize the accurate control of the quad-rotor UAV in the turbulence environment.
Aiming at the problem that the quad-rotor unmanned aerial vehicles(UAV) is vulnerable to be interfered by airflow when flying outside, and it is difficult to achieve accurate control. Firstly, the turbulence of the quad-rotor UAV during outdoor flight is modeled; and the influence of the turbulence is added to the UAV dynamic model. Then, the adaptive extended Kalman filter is designed to improve the filtering accuracy of the UAV attitude data by adjusting the adaptive factor of the noise covariance in real time, and the data is feedback to the PID position controller to control the UAV. The experimental results show that the established model can effectively reflect the motion law of the quadrotor UAV in the wind field environment, and the combination of the PID and AEKF can improve the anti-interference ability of the system and realize the accurate control of the quad-rotor UAV in the turbulence environment.
引文
[1] 李诚龙.多旋翼无人机高空飞行稳定控制问题研究[D].杭州:浙江大学,2016Li C L. Flight stability and automatic control of multirotor UAVs in a wide range task[D]. Hangzhou: Zhejiang University, 2016 (in Chinese)
[2] 刘云平,黄希杰,李先影,等.四旋翼飞行器的滑模PID轨迹跟踪控制[J].机械科学与技术,2017,36(12):1859-1865Liu Y P, Huang X J, Li X Y, et al. Trajectory tracking control of quad-rotor unmanned aerial vehicles based on sliding mode PID[J]. Mechanical Science and Technology for Aerospace Engineering, 2017,36(12):1859-1865 (in Chinese)
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[8] Wang S H, Yang Y. Quadrotor aircraft attitude estimation and control based on Kalman filter[C]//Proceedings of the 31th Chinese Control Conference. Hefei, China: IEEE, 2012:5634-5639
[9] 屈耀红,邢哲文,袁冬莉,等.基于悬停四旋翼位置姿态信息的风场估计方法研究[J].西北工业大学学报,2016,34(4):684-690Qu Y H, Xing Z W, Yuan D L, et al. Wind field estimation based on position and attitude information of quadrotor in hover[J]. Journal of Northwestern Polytechnical University, 2016,34(4):684-690 (in Chinese)
[10] 肖业伦.大气扰动中的飞行原理[M].北京:国防工业出版社,1993Xiao Y L. Principles of flight in atmospheric disdurbances[M]. Beijing: National Defense Industry Press, 1993 (in Chinese)
[11] 郑祥明.微型飞行器非线性飞行动力学与智能控制研究[D].南京:南京航空航天大学,2008Zheng X M. Research on nonlinear flight dynamics and intelligent flight control of micro air vehicles[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008 (in Chinese)
[12] 牛军锋.无人机组合导航系统的自适应算法研究[J].科学技术与工程,2012,12(28):7293-7297Niu J F. Adaptive filter research based on UAV integrated navigation[J]. Science Technology and Engineering, 2012,12(28):7293-7297 (in Chinese)
[13] He H W, Liu Z T, Hua Y. Adaptive extended kalman filter based fault detection and isolation for a lithium-ion battery pack[J]. Energy Procedia, 2015,75:1950-1955
[14] 胡高歌,高社生,赵岩.一种新的自适应UKF算法及其在组合导航中的应用[J].中国惯性技术学报,2014,22(3):357-361,367Hu G G, Gao S S, Zhao Y. Novel adaptive UKF and its application in integrated navigation[J]. Journal of Chinese Inertial Technology, 2014,22(3):357-361,367 (in Chinese)
[15] Johansen T A, Kristiansen R. Quadrotor attitude estimation using adaptive fading multiplicative EKF[C]//Proceedings of 2017 American Control Conference. Seattle, USA: IEEE, 2017:1227-1232
[2] 刘云平,黄希杰,李先影,等.四旋翼飞行器的滑模PID轨迹跟踪控制[J].机械科学与技术,2017,36(12):1859-1865Liu Y P, Huang X J, Li X Y, et al. Trajectory tracking control of quad-rotor unmanned aerial vehicles based on sliding mode PID[J]. Mechanical Science and Technology for Aerospace Engineering, 2017,36(12):1859-1865 (in Chinese)
[3] 黄廷国.基于扰动抑制的无人飞行器控制技术研究[D].上海:上海交通大学,2015Huang T G. Research on control technology of unmanned aerial vehicle based on disturbance rejection[D]. Shanghai: Shanghai Jiao Tong University, 2015 (in Chinese)
[4] 齐鹏远,王勇,张代兵.基于LADRC的无人机高精度定高控制[J].北京航空航天大学学报,2016,42(11):2472-2480Qi P Y, Wang Y, Zhang D B. Precise height control for UAV based on LADRC[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016,42(11):2472-2480 (in Chinese)
[5] 何勇灵,陈彦民,周岷峰.四旋翼飞行器在风场扰动下的建模与控制[J].中国惯性技术学报,2013,21(5):624-630He Y L, Chen Y M, Zhou M F. Modeling and control of a quadrotor helicopter under impact of wind disturbance[J]. Journal of Chinese Inertial Technology, 2013,21(5):624-630 (in Chinese)
[6] 吴瀚文.四旋翼飞行器抗风控制研究[D].哈尔滨:哈尔滨工业大学,2016Wu H W. Research on the wind control for quadrotor UAV[D]. Harbin: Harbin Institute of Technology, 2016 (in Chinese)
[7] Waslander S L, Wang C. Wind disturbance estimation and rejection for quadrotor position control[C]//Proceedings of AIAA Infotech@Aerospace Conference, Infotech@Aerospace Conference. Seattle, Washington: AIAA, 2013:1-14
[8] Wang S H, Yang Y. Quadrotor aircraft attitude estimation and control based on Kalman filter[C]//Proceedings of the 31th Chinese Control Conference. Hefei, China: IEEE, 2012:5634-5639
[9] 屈耀红,邢哲文,袁冬莉,等.基于悬停四旋翼位置姿态信息的风场估计方法研究[J].西北工业大学学报,2016,34(4):684-690Qu Y H, Xing Z W, Yuan D L, et al. Wind field estimation based on position and attitude information of quadrotor in hover[J]. Journal of Northwestern Polytechnical University, 2016,34(4):684-690 (in Chinese)
[10] 肖业伦.大气扰动中的飞行原理[M].北京:国防工业出版社,1993Xiao Y L. Principles of flight in atmospheric disdurbances[M]. Beijing: National Defense Industry Press, 1993 (in Chinese)
[11] 郑祥明.微型飞行器非线性飞行动力学与智能控制研究[D].南京:南京航空航天大学,2008Zheng X M. Research on nonlinear flight dynamics and intelligent flight control of micro air vehicles[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008 (in Chinese)
[12] 牛军锋.无人机组合导航系统的自适应算法研究[J].科学技术与工程,2012,12(28):7293-7297Niu J F. Adaptive filter research based on UAV integrated navigation[J]. Science Technology and Engineering, 2012,12(28):7293-7297 (in Chinese)
[13] He H W, Liu Z T, Hua Y. Adaptive extended kalman filter based fault detection and isolation for a lithium-ion battery pack[J]. Energy Procedia, 2015,75:1950-1955
[14] 胡高歌,高社生,赵岩.一种新的自适应UKF算法及其在组合导航中的应用[J].中国惯性技术学报,2014,22(3):357-361,367Hu G G, Gao S S, Zhao Y. Novel adaptive UKF and its application in integrated navigation[J]. Journal of Chinese Inertial Technology, 2014,22(3):357-361,367 (in Chinese)
[15] Johansen T A, Kristiansen R. Quadrotor attitude estimation using adaptive fading multiplicative EKF[C]//Proceedings of 2017 American Control Conference. Seattle, USA: IEEE, 2017:1227-1232