文献类型：期刊文献

英文题名：Vision-Based Tracking Control of Quadrotor with Backstepping Sliding Mode Control

作者：Zhao, Bingfeng[1];Tang, Yang[1];Wu, Chunping[2];Du, Wei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China

年份：2018

卷号：6

起止页码：72439

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20184706117097);WOS:【SCI-EXPANDED(收录号:WOS:000453709300001)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0809302, in part by the National Natural Science Foundation of China under Grant 61751305 and Grant 61673176, in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and in part by the Alexander von Humboldt Foundation of Germany.

语种：英文

外文关键词：Backstepping; quadrotor; Kalman filter; semi-direct monocular visual odometry; sliding mode control; tracking control

摘要：Vision-based quadrotor will be a good carrier for big data. This paper investigates the quadrotor tracking control by designing an adaptive sliding mode controller based on the backstepping technique with the advantages of simplicity in design and ease of application. A sliding mode controller is first developed to ensure fast convergence speed with the desired reference, and then the backstepping technique is used until the desired reference trajectory is achieved and finally the appropriate control laws are obtained. In order to achieve the precise and fast localization of a quadrotor, a popular visual odometry algorithm is applied to gathering good position information required in motion estimation. We employ Kalman filter for sensor data fusion and state estimation. Gazebo is applied by creating a 3D dynamic environment to recreate the complex environment potentially encountered in the real world.