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Hybrid Force and Position Control of Robotic Manipulators Using Passivity Backstepping Neural Networks

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4491))

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Abstract

This paper presents a method of force/position control by using the backstepping and passivity strict-feedback neural networks technique; passivity monitor can evaluate stability of a system based on the concept of passivity. The parameters estimation for the design is made by the neural networks technology, using the decouple method and matrix transforming technology, decomposing the robot system as the position subsystem and the force subsystem, then the control law of these subsystems are designed respectively. The results obtained are satisfactory by using hybrid force and position control, the error is negligible and the global stability of the system can also be obtained.

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References

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Author information

Authors and Affiliations

  1. Yanshan University, Qinhuangdao 066004, China

    Shu-Huan Wen & Bing-yi Mao

Authors
  1. Shu-Huan Wen
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  2. Bing-yi Mao
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering (M/C 154), University of Illinois at Chicago, 851 S. Morgan Street, 60607-7053, Chicago, IL, USA

    Derong Liu

  2. School of Automation, Southeast University, 210096, Nanjing, China

    Shumin Fei

  3. Laboratory of Complex Systems, Institute of Automation, Chinese Adacemy of Sciences, 100080, Beijing, P. R. China

    Zeng-Guang Hou

  4. School of Information Science and Engineering, Northeast University, Shenyang, 110004, China

    Huaguang Zhang

  5. School of Electrical Engineering, Hohai University, Nanjing, 210098, China

    Changyin Sun

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© 2007 Springer-Verlag Berlin Heidelberg

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Wen, SH., Mao, By. (2007). Hybrid Force and Position Control of Robotic Manipulators Using Passivity Backstepping Neural Networks. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_100

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  • DOI: https://doi.org/10.1007/978-3-540-72383-7_100

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72382-0

  • Online ISBN: 978-3-540-72383-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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