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Dual-layer fuzzy control architecture for the CAS rover arm

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  • Robotics and Automation
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Abstract

Since the conventional impedance control method for a rover arm is not suitable for unconstructed environment with uncertainties, a fuzzy inference method which improves the impedance model dynamically is introduced to realize high-precision control. The fuzzy PD control algorithm which applies to the joint control of a rover arm is analyzed in this paper. With the two level control algorithms, a novel dual-layer fuzzy control framework is proposed, which can enhance the control performance significantly. In order to verify the validity and reliability of the designed algorithms, the robotic arm of the CAS rover is considered as an experimental platform. Kinematics and dynamics models of robotic arm are derived at first. Moreover, the fuzzy inference mechanism and implementation process of impedance model parameters are illustrated. Extensive simulations and experimental results show that the control accuracy and the force control of the system have been significantly improved with the proposed dual-layer fuzzy control architecture.

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Author information

Authors and Affiliations

  1. School of Information Science & Engineering, Shenyang Ligong University, Shenyang, 110159, China

    Hongwei Gao & Kun Hong

  2. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Hongwei Gao & Kun Hong

  3. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Jinguo Liu, Yangmin Li & Yang Zhang

  4. Department of Electromechanical Engineering, University of Macau, Avenida da Universidade, Taipa, Macao, 999078, China

    Yangmin Li

Authors
  1. Hongwei Gao
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  2. Jinguo Liu
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  3. Yangmin Li
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  4. Kun Hong
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  5. Yang Zhang
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Corresponding authors

Correspondence to Jinguo Liu or Yangmin Li.

Additional information

Recommended by Associate Editor Youngjin Choi under the direction of Editorin-Chief Young-Hoon Joo.

This work was supported in part by the National Science Foundation of China (51175494, 61128008), China Postdoctoral Science Foundation Funded Project (Grant No.2013M530954), the State Key Laboratory of Robotics Foundation (Grant No.O8A120S, 2012017), Program for Liaoning Excellent Talents in University (Grant No.LJQ2014021), Liaoning Province Natural Science Fund Project(Grant No.2014020093), and Shenyang Ligong University Computer Application Key Discipline Foundation (Grant No.47710 04kfx09), Macao Science and Technology Development Fund (108/ 2012/A3, 110/2013/A3), Research Committee of University of Macau (MYRG203(Y1-L4)-FST11- LYM, MYRG183(Y1-L3)FST 11-LYM).

Hongwei Gao received his Ph.D. degree in the field of pattern recognition and intelligent system from Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS) in 2007. Since September 2009, he has been an Associate Professor of Information Science & Engineering College, Shenyang Ligong University. Currently, he is the leader of academic direction for optical and electrical measuring technology and system. His research interests include digital image processing and analysis, stereo vision and intelligent computation. He has published more than sixty technical papers in these areas as first authors or co-authors.

Jinguo Liu received his Ph.D. degree in robotics from Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS) in 2007. Since January 2011, he has been a Full Professor with SIA, CAS. He also holds the Assistant Director position of State Key Laboratory of Robotics (China) from March 2008. His research interests include modular robot, rescue robot, space robot, and bio-inspired robot. He has authored and coauthored about sixty papers and twenty patents in above areas.

Yangmin Li received his B.S. and M.S. degrees from the Mechanical Engineering Department, Jilin University, Changchun, China in 1985 and 1988 respectively. He received his Ph.D. degree from the Mechanical Engineering Department, Tianjin University, Tianjin, China in 1994. After that, he worked as Postdoctoral Research Associate in Purdue University, USA. He is currently a Full Professor and Director of Mechatronics Laboratory at, Faculty of Science and Technology of University of Macau. He is also a Visiting Professor at Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS). His research interests are mobile robots, swarm intelligence, and micro/nano manipulation. Up to now he has published 325 papers in refereed book chapters, journals and conferences. He is an IEEE senior member and a member of ASME and CSME. He was a Technical Editor of IEEE/ ASME Transactions on Mechatronics, Associate Editor of IEEE Transactions on Automation Science Engineering. He is serving as Associate Editor of International Journal of Control, Automation, and Systems, Council Member and Editor of Chinese Journal of Mechanical Engineering, Associate Editor of IEEE Access.

Kun Hong born in 1989. He is a postgraduate student of Shenyang Ligong University. His main research interests include image processing, compliance control and intelligent control theory.

Yang Zhang received his B.S and Ph.D. degrees from Mechatronics Engineering, Zhejiang University, Hangzhou, China, in 2005 and 2011, respectively. Since 2013, he is a Postdoctor and Assistant Professor in State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science. His research interests include mechatronics system of robotics and intelligent system.

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Gao, H., Liu, J., Li, Y. et al. Dual-layer fuzzy control architecture for the CAS rover arm. Int. J. Control Autom. Syst. 13, 1262–1271 (2015). https://doi.org/10.1007/s12555-013-9413-4

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  • DOI: https://doi.org/10.1007/s12555-013-9413-4

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