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Adaptive Neuro Integral Sliding Mode Control on Synchronization of Two Robot Manipulators

  • Parvaneh Esmaili
  • Habibollah Haron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

Designing a new adaptive synchronization controller for multiple robot manipulators is main purpose of this study. But, this synchronization between robots are considered without having direct communication between robots. The adaptive synchronization method is consisted of the integral sliding mode controller improved with adaptive neural network controller. In order to analyze the performance of the proposed method, four different situations are considered. Also, the result are compared with the ANFIS method. The proposed method is guaranteed by Lyapunov stability method.

Keywords

Synchronization controller Adaptive neural network controller Integral sliding mode control 

References

  1. 1.
    Armstrong, B., Khatib, O., Burdick, J.:. The explicit dynamic model and inertial parameters of the PUMA 566 arm. In: IEEE, pp. 510–518 (1986)Google Scholar
  2. 2.
    Bouteraa, Y.: Adaptive backstepping synchronization for networked Lagrangian systems. Int. J. Comput. Appl. 42(12), 1–8 (2012)Google Scholar
  3. 3.
    Esmaili, P., Haron, H.: Intelligent synchronization tool using ANFIS for multi robot manipulators. In: Fujita, H. (ed.) New Trends in Software Methodologies, Tools and Techniques, pp. 37–50. IOS Press, Amsterdam (2014). doi: 10.3233/978-1-61499-434-3-37sCrossRefGoogle Scholar
  4. 4.
    Kim, J., Iwamoto, K., Kuffner, J.J., Ota, Y., Pollard, N.S.: Physically based grasp quality evaluation under pose uncertainity. IEEE Trans. Rob. 29, 1424–1439 (2013)CrossRefGoogle Scholar
  5. 5.
    Siqueira, A.A.G., Terra, M.H.: Neural network-based control for fully actuated and underactuated cooperative manipulators. Control Eng. Pract. 17(3), 418–425 (2009)CrossRefGoogle Scholar
  6. 6.
    Sun, D.: Synchronization and Control of Multiagent Systems. CRC Press, Boca Raton (2010)CrossRefGoogle Scholar
  7. 7.
    Utkin, V., Shi, J.S.J.: Integral sliding mode in systems operating under uncertainty conditions. In: Proceedings of 35th IEEE Conference on Decision and Control, vol. 4, pp. 1–6 (1996)Google Scholar
  8. 8.
    Zeinali, M., Notash, L.: Adaptive sliding mode control with uncertainty estimator for robot manipulators. Mech. Mach. Theory 45(1), 80–90 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zhao, D., Li, C., Zhu, Q.: Low-pass-filter-based position synchronization sliding mode control for multiple robotic manipulator systems. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 225(8), 1136–1148 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhao, D., Zhu, Q.: Position synchronized control of multiple robotic manipulators based on integral sliding mode. Int. J. Syst. Sci. 45(3), 556–570 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhao, D., Zhu, Q., Li, N., Li, S.: Synchronized control with neuro-agents for leader–follower based multiple robotic manipulators. Neurocomputing 124, 149–161 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of EngineeringGirne American UniversityKyreniaCyprus
  2. 2.Department of Computer Science, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

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