AUV Vertical Motion Control Based on Kalman Filtering

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 海洋信息技术装备中心

	AUV Vertical Motion Control Based on Kalman Filtering
	Guo JQ(国婧倩)1; Zheng R(郑荣)3 ; Wei AB(魏奥博)2,3
	2018
会议日期	August 16, 2018
会议地点	Beijing, China
页码	334-339
英文摘要	AUV docking is a hot topic in underwater robot research. In order to fulfill the mission of docking, AUV needs to have more precise vertical navigation control ability, reduce the depth of the sensor there is a big noise data when calculating the error and AUV vertical depth when motion is not smooth. In this paper, the kalman filter is integrated into the motion control of vertical plane, and the double closed-loop PID cascade control system is designed and not based on the model. The whole control system is divided into two loops, the inner ring for the trim Angle PID controller, the output through the thrust allocation to calculate the required torque and torque, outer ring for the depth of the PID controller, the output for the input of pitch Angle. The kalman filter is integrated into the feedback loop of the depth data to improve the accuracy of the feedback data. The precision of vertical motion control is reflected by the stability of fixed depth navigation. Through the experiment on the lake, the depth mean square deviation of the vertical plane at the speed of 2kn is 0.24m2, the mean square deviation of the vertical Angle is 0.18 degree2 which proves the feasibility of this method.
产权排序	1
会议录	Proceedings of the 1st WRC Symposium on Advanced Robotics and Automation 2018
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5386-7674-5
WOS记录号	WOS:000457712000054
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/23782]
专题	沈阳自动化研究所_海洋信息技术装备中心
通讯作者	Wei AB(魏奥博)
作者单位	1.Northeastern University, Shenyang, CO 110819 China 2.University of Chinese Academy of Sciences, Beijing 100049 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, CO 110016 China
推荐引用方式 GB/T 7714	Guo JQ,Zheng R,Wei AB. AUV Vertical Motion Control Based on Kalman Filtering[C]. 见:. Beijing, China. August 16, 2018.