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Neural Network Compensation of Dynamic Errors in a Robot Manipulator Programmed Control System

  • Yan Zhengjie
  • Ekaterina N. Rostova
  • Nikolay V. RostovEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

The subject of consideration in this paper is a programmed control system of a robot manipulator. Mathematical description of the control system was presented taking into account the nonlinear dynamics of the robot mechanism. Synthesis of multivariable compensators of dynamic errors for a prototype control system was carried out. Computer models of the control system with synthesized compensators were developed using MATLAB package. The results of teaching of neural network compensators are given for a programmed trajectory of the robot gripper. Comparative analysis of dynamic errors in the prototype system and the system with neural network compensators was conducted.

Keywords

Robot manipulator Programmed control system Neural network Nonlinear multivariable compensators Simulation Dynamic analysis Dynamic errors 

References

  1. 1.
    Bhattacharjee, T., Bhattacharjee, A.: A study of neural network based inverse kinematics solution for a planar three joint robot with obstacle avoidance. Assam Univ. J. Sci. Technol.: Phys. Sci. Technol. 5(2), 1–7 (2010)Google Scholar
  2. 2.
    Chiddarwar, S.S., Babu, N.R.: Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach. Eng. Appl. Artif. Intell. 23, 1083–1092 (2010)CrossRefGoogle Scholar
  3. 3.
    Corke, P.I.: Robotics, Vision and Control. Fundamental Algorithms in MATLAB, 2nd edn. Springer, Heidelberg (2017)CrossRefGoogle Scholar
  4. 4.
    Corke, P.I.: Robotics Toolbox for MATLAB. Release 10 (2017)Google Scholar
  5. 5.
    Dixon, W.E.; Moses, D.; Walker, L.D.; Dawson, D.M.: A Simulink-based robotic toolkit for simulation and control of the PUMA 560 robot manipulator. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, A, pp. 2202–2207 (2001)Google Scholar
  6. 6.
    Duc, M.N, Trong, T.N.: Neural network structures for identification of nonlinear dynamic robotic manipulator. In: Proceedings of IEEE International Conference on Mechatronics and Automation, pp. 1575–1580 (2014)Google Scholar
  7. 7.
    Farzam, T., Nafise, F.R.: Robust control of a 3-DOF parallel cable robot using an adaptive neuro-fuzzy inference system. In: Artificial Intelligence and Robotics (IRANOPEN) (2017)Google Scholar
  8. 8.
    Ignatova, E.I., Lopota, A.V., Rostov, N.V.: Robot Motion Control Systems. Computer-Aided Design. Polytechnic Publishing Center, St. Petersburg (2014)Google Scholar
  9. 9.
    Ishmuratov, V.N., Rostov, N.V.: Computer training of neural network quasi time-optimal digital regulators. In: Proceedings of International Scientific and Practical Conference, pp. 134–136. Polytechnic Publishing Center, St. Petersburg (2007)Google Scholar
  10. 10.
    Islam, S., Liu, X.P.: Robust sliding mode control for robot manipulators. Proc. IEEE Trans Ind Electron. 58, 2444–2453 (2011)CrossRefGoogle Scholar
  11. 11.
    Kim, Y.H., Lewis, F.L.: Neural network output feedback control of robot manipulators. Proc. IEEE Trans Robot Autom. 15, 301–309 (1999)CrossRefGoogle Scholar
  12. 12.
    Kumar, N., Panwar, V., Borm, J.H., Chai, J.: Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound. Appl. Math. Comput. 231, 320–328 (2014)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Lee, G.W., Cheng, F.T.: Robust control of manipulators using the computed torque plus H∞ compensation method. IEEE Proc. Control Theory Appl. 143(1), 64–72 (1996)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Lewis, F.L., Abdallah, C.T., Dawson, D.M.: Control of Robot Manipulators. Macmillan, New York (1993)Google Scholar
  15. 15.
    Lewis, F.L., Jaganathan, S., Yesildirek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems (1999)Google Scholar
  16. 16.
    Santibañez, V., Camarillo, K., Moreno-Valenzuela, J., Campa, R.: A practical PID regulator with bounded torques for robot manipulator. Int. J. Control Autom. Syst. 8(3), 544–555 (2010)CrossRefGoogle Scholar
  17. 17.
    Seshagiri, S., Khalil, H.K.: Output feedback control of nonlinear systems using RBF neural networks. Proc. IEEE Trans Neural Netw. 11, 69–79 (2000)CrossRefGoogle Scholar
  18. 18.
     Singh, H.P, Sukavanam, N, Panwar, V.: Neural network based compensator for robustness to the robot manipulators with uncertainties. In: Proceedings of International Conference on Mechanical and Electrical Technology, pp. 444–448 (2010)Google Scholar
  19. 19.
    Tai, N.T., Ahn, K.K.: A RBF neural network sliding mode controller for SMA actuator. Proc. Int J Control Autom. Syst. 8, 1296–1305 (2010)CrossRefGoogle Scholar
  20. 20.
    Terpukhov, S.Yu., Rostov, N.V.: Neural network interpolation of program trajectories of links of a robot manipulator. In: Proceedings of International Scientific and Practical Conference, pp. 70–72. Polytechnic Publishing Center, St. Petersburg (2010)Google Scholar
  21. 21.
    Yan, Z., Rostov, N.V.: Error analysis of neural network interpolators of program trajectories of links of a robot manipulator. In: Proceedings of ComCon 2018, pp. 114–119. Polytechnic Publishing Center, St. Petersburg. (2018)Google Scholar
  22. 22.
    Yurevich, E.I.: Fundamentals of Robotics. BHV, St. Petersburg (2005)Google Scholar
  23. 23.
    Zenkevich, S.L., Yuschenko, A.S.: Fundamentals of Robot Manipulator Control. MSTU by name N. E. Bauman, Moscow (2005)Google Scholar
  24. 24.
    Zhang, Y., Zhu, H., Lv, X., Li, K.: Joint angle drift problem of Puma560 robot arm solved by a simplified LVI-based primal-dual neural network. In: Proceedings of IEEE International Conference on Industrial Technology (2008)Google Scholar
  25. 25.
    Zhang, Y., Wang, J.: Obstacle avoidance for kinematically redundant manipulators using a dual neural network. Proc. IEEE Trans Syst. Man Cybern. Part B Cybern. 34(1), 752–759 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yan Zhengjie
    • 1
  • Ekaterina N. Rostova
    • 2
  • Nikolay V. Rostov
    • 1
    Email author
  1. 1.Peter the Great St. Petersburg Polytechnic UniversitySaint PetersburgRussia
  2. 2.St. Petersburg Institute for Informatics and Automation of the Russian Academy of ScienceSaint PetersburgRussia

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