Image-based prescribed performance visual servoing control of a QUAV with hysteresis quantised input

ABSTRACT

In this paper, a novel image-based prescribed performance visual servoing control scheme with hysteresis quantised input is proposed for quadrotor unmanned aerial vehicles (QUAVs, for short). First, based on the perspective projection principle, effective image features are defined on a image plane called virtual image plane, and the decoupled image feature dynamics are achieved with respect to target points. Then, the image feature dynamics and QUAV dynamics are combined to derive a nonlinear dynamic system, and the nonlinear system is decoupled into two subsystems based on the commonly used inner–outer loop control framework. Furthermore, the prescribed performance control-based visual servoing control scheme with hysteresis quantizer is designed, and the necessary stability proof is presented. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.

KEYWORDS:

• Hysteresis quantizer
• image-based visual servo
• inner–outer loop control
• prescribed performance control

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study. If anyone is interested in our Matlab simulation, please contact the first author, we are glad to provide the Matlab code.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China under Grant 62103221 and by the Post Doctoral Innovative Talent Support Program under Grant BX2021157.

Notes on contributors

Jiannan Chen

Jiannan Chen received the bachelor's degree in mechanical engineering and automation from the Beijing Union University, the master's and Ph.D. degrees in control theory and control engineering from Yanshan University. He is now a lecturer in Yanshan University. His research interests include EEG and EMG based human–computer interaction, deep learning, nonlinear control, and the control of UAVs.

Fuchun Sun

Fuchun Sun is currently a Full Professor with the Department of Computer Science and Technology, Tsinghua University, Beijing, China. His research interests include intelligent control and robotics. Prof. Sun was a recipient of the National Science Fund for Distinguished Young Scholars. He serves as the Editor-in-Chief for Cognitive Computation and Systems and an Associate Editor for a series of international journals, including the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS and the IEEE TRANSACTIONS ON FUZZY SYSTEMS, MECHATRONICS, AND ROBOTICS AND AUTONOMOUS SYSTEMS.

Changchun Hua

Changchun Hua received the Ph.D. degree in electrical engineering from Yanshan University, Qinhuangdao, China, in 2005. He was a Research Fellow with the National University of Singapore, Singapore, from 2006 to 2007. From 2007 to 2009, he worked with Carleton University, Ottawa, ON, Canada, funded by Province of Ontario Ministry of Research and Innovation Program. From 2009 to 2011, he worked with the University of Duisburg-Essen, Duisburg, Germany, funded by Alexander von Humboldt Foundation. He is currently a Full Professor with Yanshan University. He has authored or co-authored more than 110 papers in mathematical, technical journals, and conferences. He has been involved in more than ten projects supported by the National Natural Science Foundation of China, the National Education Committee Foundation of China, and other important foundations. His research interests are in nonlinear control systems, control systems design over network, teleoperation systems, and intelligent control.