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Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

  • Mechanical Engineering, Control Science and Information Engineering
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Abstract

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Changchun University of Technology, Changchun, 130012, China

    Yuan-chun Li  (李元春)

  2. Department of Control Science and Engineering, Jilin University, Changchun, 130012, China

    Yuan-chun Li  (李元春) & Gui-bin Ding  (丁贵彬)

  3. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Bo Zhao  (赵博)

Authors
  1. Yuan-chun Li  (李元春)
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  2. Gui-bin Ding  (丁贵彬)
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  3. Bo Zhao  (赵博)
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Corresponding author

Correspondence to Bo Zhao  (赵博).

Additional information

Foundation item: Project(61374051, 61603387) supported by the National Natural Science Foundation of China; Projects(20150520112JH, 20160414033GH) supported by the Scientific and Technological Development Plan in Jilin Province of China; Project(20150102) supported by Opening Funding of State Key Laboratory of Management and Control for Complex Systems, China

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Li, Yc., Ding, Gb. & Zhao, B. Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators. J. Cent. South Univ. 23, 2917–2925 (2016). https://doi.org/10.1007/s11771-016-3355-y

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  • DOI: https://doi.org/10.1007/s11771-016-3355-y

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