Abstract
Most of the existing identification/control algorithm of uncertain robot manipulators have been proposed to achieve model identification and trajectory tracking with expected precision, but the convergence time and transient tracking performance have been rarely discussed. In this paper, an adaptive fixed-time estimation algorithm is proposed for an uncertain robot. A recursive update law combined with an auxiliary filtering technique has been exploited such that the measurement of acceleration signals could be avoided during the estimation process. Based on the results of parameter identification, we propose a fixed-time control scheme which can guarantee the specified motion performance and prescribed convergence time simultaneously. The tiny practical error of parameter identification can be effectively handled with the proposed control scheme. Finally, the simulation results based on an uncertain 2-DOF robot have demonstrated the effectiveness of the proposed identification/control algorithm.
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This work was partially supported by Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S001913, and National Natural Science Foundation of China under Grant 62003136, and National Key Research and Development Rrogram 2020YFB1712600, and Natural Science Foundation of Changsha under Grant kq2014060.
Chengzhi Zhu received his B.S. degree in automation from the Qinghai University, Xining, China, in 2018. He has been an exchange student in East China University of Science and Technology, Shanghai, China, from 2015–2016. He is currently pursuing a Ph.D. degree in South China University of Technology, China, and major in control science and engineering. His current research interest lies in robotics, intelligent control and humanrobot interaction.
Yiming Jiang received his B.S. degree in automation from the Hunan University, Changsha, China, in 2011, an M.S. degree in control theory and engineering from the School of Automation, Guangdong University of Technology, Guangzhou, China, in 2015 and a Ph.D. degree from the School of Control Science and Engineering, South China University of Technology, Guangzhou, China, in 2019. He has been a visiting scholar in University of Portsmouth, UK, in 2017–2018. He is currently a postdoctoral in College of Electrical and Information Engineering at Hunan University. His research interests include robotics, intelligent control and human-robot interaction.
Chenguang Yang is a Professor of Robotics. He received his Ph.D. degree in control engineering from the National University of Singapore, Singapore, in 2010, and postdoctoral training in human robotics from the Imperial College London, London, U.K. He was awarded UK EPSRC UKRI Innovation Fellowship and individual EU Marie Curie International Incoming Fellowship. As lead author, he won the IEEE Transactions on Robotics Best Paper Award (2012) and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award (2022). He is a Co-Chair of IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM) and a Co-Chair of IEEE Technical Committee on Bio-mechatronics and Bio-robotics Systems (B2S). He serves as Associate Editors of a number of international top journals including Neurocomputing and seven IEEE Transactions. His research interest lies in human robot interaction and intelligent system design.
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Zhu, C., Jiang, Y. & Yang, C. Fixed-time Parameter Estimation and Control Design for Unknown Robot Manipulators with Asymmetric Motion Constraints. Int. J. Control Autom. Syst. 20, 268–282 (2022). https://doi.org/10.1007/s12555-020-0859-x
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DOI: https://doi.org/10.1007/s12555-020-0859-x