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Neural Adaptive Force Control for Compliant Robots

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Bio-Inspired Applications of Connectionism (IWANN 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2085))

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Abstract

This paper deals with the methodological approach for the development and implementation of force-position control for robotized systems. We propose a first approach based on neural network to treat globally the problem of the adaptation of robot behavior to various classes of tasks and to actual conditions of the task where its parameters may vary. The obtained results are presented and analyzed in order to prove the efficiency of the proposed approach

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References

  1. M.Y. Amirat. Contribution à la commande de haut niveau de processus robotisés et à l’utilisation des concepts de l’IA dans l’interaction robot-environnement. PHD thesis University Paris XII, Janvier 1996.

    Google Scholar 

  2. A.G. Barto and P. Anandan. Pattern recognizing stochastic learning automata. In IEEE Transaction on Systems, Man, and Cybernetics, 360–375, 1985.

    Google Scholar 

  3. N. Chatenet and H. Bersini. Economical reinforcement learning for non stationary problems. In Lecture notes in Computer Science 1327, Artificial Neural Network ICANN’97, 7th International Conference Lausanne, Switzerland Proceeding, pp. 283–288, October 1997.

    Google Scholar 

  4. G. Cybenco. Approximations by superposition of sigmoidal function. In Advanced Robotics, Intelligent Automation and Active Systems, pp 373–378, Bremen, September 15-17, 1997.

    Google Scholar 

  5. E. Dafaoui and Y. Amirat and J. Pontnau and C. François. Analysis and Design of a six DOF Parallel Manipulator. Modelling, Singular Configurations and Workspace. In IEEE Transactions on Robotics and Automation, vol. 14, pp. 78–92, Février 1998.

    Google Scholar 

  6. J.C. Doyle and K. Glover and P.P. Khargonecker and B.A. Francis. State space solutions to the standard H2 and H∞. In IEEE Trans. On Automat. Contr., vol. 34, pp. 831–847, 1989.

    Article  MATH  Google Scholar 

  7. B. Karan. Robust position/force control of robot manipulator in contact with linear dynamic environment, In Proc. of the Third ECPD International Conference on Advanced Robotics, Intelligent Automation and Active Systems, pp 373–378, Bremen, September 15-17, 1997.

    Google Scholar 

  8. O. Khatib. A unified approach to motion and force control of robot manipulators. In IEEE J. Robot Automation, 43–53, 1987.

    Google Scholar 

  9. S. Komada and al.. Robust force control based on estimation of environment. In IEEE Int. Conf. on Robotics and Automation, pp. 1362–1367, Nice, France, May,1992.

    Google Scholar 

  10. L. Laval and N.K. M’sirdi. Modeling, identification and robust force control of hydraulic actuator using H∞?approach. In Proceeding of IMACS, Berlin, Germany, 1995.

    Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Saadia, N., Amirat, Y., Pontnaut, J., Ramdane-Cherif, A. (2001). Neural Adaptive Force Control for Compliant Robots. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_52

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  • DOI: https://doi.org/10.1007/3-540-45723-2_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42237-2

  • Online ISBN: 978-3-540-45723-7

  • eBook Packages: Springer Book Archive

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