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Neural network-based compensation control of mobile robots with partially known structure

Neural network-based compensation control of mobile robots with partially known structure

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This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov's theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.

References

    1. 1)
    2. 2)
    3. 3)
      • Hamerlain, F., Achour, K., Floquet, T., Perruquetti, W.: `Higher Order Sliding Mode Control of wheeled mobile robots in the presence of sliding effects', 44thIEEE Conf. on Decision and Control, and the European Control Conf., December 2004, Seville, Spain, p. 12–15.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • F.G. Rossomando , C. Soria , D. Patiño , R. Carelli . Model reference adaptive control for mobile robots in trajectory tracking using radial basis function neural networks. Latin Am. Applicated Res. (LAAR) , 2 , 177 - 182
    8. 8)
      • Wu, W., Chen, H., Wang, Y., Woo, P.: `Adaptive exponential stabilization of mobile robots with uncertainties', Proc. IEEE 38th Conf. on Decision and Control, 1999, Phoenix, Arizona, USA, p. 3484–3489.
    9. 9)
    10. 10)
      • Dong, W., Huo, W.: `Tracking control of wheeled mobile robots with unknown dynamics', Proc. IEEE Int. Conf. on Robotics & Automation, 1999, Detroit, Michigan, p. 2645–2650.
    11. 11)
      • Kim, M.S., Shin, J.H., Lee, J.J.: `Design of a robust adaptive controller for a mobile robot', Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2000, p. 1816–1821.
    12. 12)
      • J.J.E. Slotine , W. Li . (1991) Applied nonlinear control.
    13. 13)
    14. 14)
      • Dong, W., Guo, Y.: `Dynamic tracking control of uncertain mobile robots', IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2005, p. 2774–2779.
    15. 15)
    16. 16)
      • De, La Cruz C., Carelli, R.: `Dynamic modeling and centralized formation control of mobile robots', 32ndAnnual Conf. on IEEE Industrial Electronics Society IECON, 2006, Paris.
    17. 17)
      • Oliveira, de V.M., De, Pieri E.R., Lages, W.F.: `Mobile robot control using sliding mode and neural network', Proc. Seventh IFAC Symp. on Robot Control, 2003, Wroclaw, p. 581–586.
    18. 18)
    19. 19)
    20. 20)
      • W.-S. Lin , C.-S. Chen . Robust adaptive sliding mode control using fuzzy modelling for a class of uncertain MIMO nonlinear systems. IEE Proc. – Control Theory Appl. , 193 - 201
    21. 21)
      • Künhe, F., Gomes, J., Fetter, W.: `Mobile robot trajectory tracking using model predictive control', Second IEEE Latin-American Robotics Symp., 2005, São Luis, Brazil.
    22. 22)
    23. 23)
    24. 24)
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