Introduction

  • Ramon Garcia-Hernandez
  • Michel Lopez-Franco
  • Edgar N. Sanchez
  • Alma Y. Alanis
  • Jose A. Ruz-Hernandez
Chapter
First Online:
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 96)

Abstract

Robot manipulators are employed in a wide range of applications such as in manufacturing to move materials, parts, and tools of various types. Future applications will include nonmanufacturing tasks, as in construction, exploration of space, and medical care.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Akhavan, S., Jamshidi, M.: ANN-based sliding mode control for non-holonomic mobile robots. In: Proceedings of the IEEE International Conference on Control Applications, pp. 664–667. Anchorage, Alaska (2000)Google Scholar
  2. 2.
    Alanis, A.Y., Sanchez, E.N., Loukianov, A.G., Chen, G.: Discrete-time output trajectory tracking by recurrent high-order neural network control. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp. 6367–6372. San Diego, CA, USA (2006)Google Scholar
  3. 3.
    Alanis, A.Y., Sanchez, E.N., Loukianov, A.G.: Discrete-time adaptive backstepping nonlinear control via high-order neural networks. IEEE Trans. Neural Netw. 18(4), 1185–1195 (2007)CrossRefGoogle Scholar
  4. 4.
    Benitez, V.H.: Decentralized Continuous Time Neural Control. Ph.D. thesis, Cinvestav, Unidad Guadalajara, Guadalajara, Jalisco, Mexico (2010)Google Scholar
  5. 5.
    Benitez, V.H., Sanchez, E.N., Loukianov, A.G.: Decentralized adaptive recurrent neural control structure. Eng. Appl. Artif. Intell. 20(8), 1125–1132 (2007)CrossRefGoogle Scholar
  6. 6.
    Campos, J., Lewis, F.L., Selmic, R.: Backlash compensation with filtered prediction in discrete time nonlinear systems using dynamic inversion by neural networks. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1289–1295, San Francisco, CA, USA (2000)Google Scholar
  7. 7.
    Chen, W., Li, J.: Decentralized output-feedback neural control for systems with unknown interconnections. IEEE Trans. Syst. Man Cybern. part B 38(1), 258–266 (2008)CrossRefGoogle Scholar
  8. 8.
    Cook, G.: Mobile Robots: Navigation, Control and Remote Sensing. Wiley, Hoboken (2011)CrossRefGoogle Scholar
  9. 9.
    Do, K.D., Jiang, Z.P., Pan, J.: Simultaneous tracking and stabilization of mobile robots: an adaptive approach. IEEE Trans. Autom. Control 49(7), 1147–1151 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Feldkamp, L.A., Prokhorov, D.V., Feldkamp, T.M.: Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks. Neural Netw. 16(5), 683–689 (2003)CrossRefGoogle Scholar
  11. 11.
    Fierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Netw. 9(4), 589–600 (1998)CrossRefGoogle Scholar
  12. 12.
    Fu, L.-C.: Robust adaptive decentralized control of robot manipulators. IEEE Trans. Autom. Control 37(1), 106–110 (1992)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gavel, D.T., Siljak, D.D.: Decentralized adaptive control: structural conditions for stability. IEEE Trans. Autom. Control 34(4), 413–426 (1989)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Ge, S.S., Zhang, J., Lee, T.H.: Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time. IEEE Trans. Syst. Man Cybern. part B 34(4), 1630–1645 (2004)CrossRefGoogle Scholar
  15. 15.
    Gourdeau, R.: Object-oriented programming for robotic manipulator simulation. IEEE Robot. Autom. 4(3), 21–29 (1997)CrossRefGoogle Scholar
  16. 16.
    Holland, J.: Designing Autonomous Mobile Robots: Inside the Mind of an Intelligent Machine. Newnes, Burlington (2003)Google Scholar
  17. 17.
    Huang, S., Tan, K.K., Lee, T.H.: Decentralized control design for large-scale systems with strong interconnections using neural networks. IEEE Trans. Autom. Control 48(5), 805–810 (2003)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jagannathan, S.: Control of a class of nonlinear discrete-time systems using multilayer neural networks. IEEE Trans. Neural Netw. 12(5), 1113–1120 (2001)CrossRefGoogle Scholar
  19. 19.
    Jevtic, A., Gutierrez, A., Andina, D., Jamshidi, M.: Distributed bees algorithm for task allocation in swarm of robots. IEEE Syst. J. 6(2), 296–304 (2012)CrossRefGoogle Scholar
  20. 20.
    Jiang, Z.-P.: New results in decentralized adaptive nonlinear control with output feedback. In: Proceedings of the 38th IEEE Conference on Decision and Control, pp. 4772–4777, Phoenix, AZ, USA (1999)Google Scholar
  21. 21.
    Jiang, Z.-P., Nijmeijer, H.: A recursive technique for tracking control of nonholonomic systems in chained form. IEEE Trans. Autom. Control 44(2), 265–279 (1999)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Jin, Y.: Decentralized adaptive fuzzy control of robot manipulators. IEEE Trans. Syst. Man Cybern. Part B 28(1), 47–57 (1998)CrossRefGoogle Scholar
  23. 23.
    Karakasoglu, A., Sudharsanan, S.I., Sundareshan, M.K.: Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators. IEEE Trans. Neural Netw. 4(6), 919–930 (1993)CrossRefGoogle Scholar
  24. 24.
    Kirk, D.E.: Optimal Control Theory: An Introduction. Dover Publications, Englewood Cliffs (2004)Google Scholar
  25. 25.
    Krstic, M., Kanellakopoulos, I., Kokotovic, P.V.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)MATHGoogle Scholar
  26. 26.
    Kumbla, K.K., Jamshidi, M.: Neural network based identification of robot dynamics used for neuro-fuzzy controller. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1118–1123, Albuquerque, NM, USA (1997)Google Scholar
  27. 27.
    Lewis, F.L., Jagannathan, S., Yesildirek, A.: Neural Network Control of Robots Manipulators and Nonlinear Systems. Taylor & Francis Ltd, London (1999)Google Scholar
  28. 28.
    Lewis, F.L., Campos, J., Selmic, R.: Neuro-fuzzy control of industrial systems with actuator nonlinearities. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (2002)Google Scholar
  29. 29.
    Li, Y., Qiang, S., Zhuang, X., Kaynak, O.: Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Trans. Neural Netw. 15(3), 693–701 (2004)CrossRefGoogle Scholar
  30. 30.
    Liu, M.: Decentralized control of robot manipulators: nonlinear and adaptive approaches. IEEE Trans. Autom. Control 44(2), 357–363 (1999)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Ni, M.-L., Er, M.J.: Decentralized control of robot manipulators with coupling and uncertainties. In: Proceedings of the American Control Conference, pp. 3326–3330, Chicago, IL, USA (2000)Google Scholar
  32. 32.
    Ornelas, F., Loukianov, A.G., Sanchez, E.N., Bayro-Corrochano, E.: Decentralized neural identification and control for uncertain nonlinear systems: application to planar robot. J. Frankl. Inst. 347(6), 1015–1034 (2010)MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Park, B.S., Yoo, S.J., Park, J.B., Choi, Y.H.: A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots. IEEE Trans. Control Syst. Technol. 18(5), 1199–1206 (2010)CrossRefGoogle Scholar
  34. 34.
    Raghavan, V., Jamshidi, M.: Sensor fusion based autonomous mobile robot navigation. In: Proceedings of the IEEE International Conference on System of Systems Engineering, SoSE ’07, pp. 1–6, San Antonio, TX, USA (2007)Google Scholar
  35. 35.
    Safaric, R., Rodic, J.: Decentralized neural-network sliding-mode robot controller. In: Proceedings of the 26th Annual Conference on the IEEE Industrial Electronics Society, pp. 906–911, Nagoya, Aichi, Japan (2000)Google Scholar
  36. 36.
    Sanchez, E.N., Ricalde, L.J.: Trajectory tracking via adaptive recurrent neural control with input saturation. In: Proceedings of the International Joint Conference on Neural Networks, pp. 359–364. Portland, OR, USA (2003)Google Scholar
  37. 37.
    Sanchez, E.N., Gaytan, A., Saad, M.: Decentralized neural identification and control for robotics manipulators. In: Proceedings of the IEEE International Symposium on Intelligent Control, pp. 1614–1619, Munich, Germany (2006)Google Scholar
  38. 38.
    Santibañez, V., Kelly, R., Llama, M.A.: A novel global asymptotic stable set-point fuzzy controller with bounded torques for robot manipulators. IEEE Trans. Fuzzy Syst. 13(3), 362–372 (2005)CrossRefGoogle Scholar
  39. 39.
    Shaneyfelt, T., Joordens, M., Nagothu, K., Prevost, J., Kumar, A., Ghazi, S.S.M., Jamshidi, M.: Control and simulation of robotic swarms in heterogeneous environments. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 1314–1319, Suntec, Singapore (2008)Google Scholar
  40. 40.
    Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robots. Bradford Company, Scituate (2004)Google Scholar
  41. 41.
    Siljak, D.D.: Decentralized Control of Complex Systems. Academic Press, New York (1991)MATHGoogle Scholar
  42. 42.
    Yang, J.-M., Kim, J.-H.: Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Trans. Robot. Autom. 15(3), 578–587 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Ramon Garcia-Hernandez
    • 1
  • Michel Lopez-Franco
    • 2
  • Edgar N. Sanchez
    • 2
  • Alma Y. Alanis
    • 3
  • Jose A. Ruz-Hernandez
    • 1
  1. 1.Universidad Autonoma del CarmenCiudad del CarmenMexico
  2. 2.CINVESTAV Unidad GuadalajaraZapopanMexico
  3. 3.Universidad de GuadalajaraGuadalajaraMexico

Personalised recommendations