A New Adaptive Neuro-sliding Mode Control for Gantry Crane

Regular Paper Control Theory and Applications
  • 17 Downloads

Abstract

This paper presents a new adaptive neuro-sliding mode control for gantry crane as varying rope length. This control method derived from combining the sliding surfaces of three subsystem of the gantry crane (trolley position, rope length, anti-swing) to draw out two system sliding surfaces: the trolley position with the anti-swing and the rope length and the anti-swing. On the based of the sliding mode control principle, drawn out the equivalent controller and the switching controller for gantry crane. But due to the uncertain parameters-nonlinear model of gantry crane with the bound disturbances, combining the neural approximate method, defined the neural controller and the compensation controller for the difference between the equivalent controller and the neural controller for two system control inputs: trolley position and rope length. The adaptive control laws for these controllers were deduced from Lyapunov’s stable criteria to asymptotically stabilize the sliding surfaces. Simulation studies are performed to illustrate the effectiveness of the proposed control.

Keywords

Adaptive controller artificial neural networks gantry crane sliding mode control stability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    P. Hahn, D. Chwa, and K. Hong, “A feedback linearization control of container cranes: varying rope length,” Interna tional Journal of Control, Automation and Systems, vol. 5, no. 4, pp. 379–387, 2007.Google Scholar
  2. [2]
    D. Liu and W. Guo, “Nonlinear controller design for the underactuated crane system,” International Journal of Control and Automation, vol. 6, no. 6, pp. 93–104, 2013.CrossRefGoogle Scholar
  3. [3]
    D. Qian and J. Yi, “Design of combining sliding mode controller for overhead crane systems,” International Journal of Control and Automation, vol. 6, no. 1, pp. 131–140, 2013.Google Scholar
  4. [4]
    A. Kaur, S. Kumari, and T. Singh, “Position control of overhead cranes using fuzzy controller,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 5, pp. 9341–9350, 2014.Google Scholar
  5. [5]
    A. Pal and R. K. Mudi, “An adaptive PD-type FLC and its real time implementation to overhead crane control,” International Association of Scientific Innovation and Research, vol. 13, 2013.Google Scholar
  6. [6]
    M. Nazemizadeh, “A PID tuning method for tracking control of an underactuated gantry crane,” Universal Journal of Engineering Mechanics, vol. 1, no. 3, pp. 45–49, 2013.Google Scholar
  7. [7]
    J. Qiu, H. Gao, and S. X. Ding, “Recent advances on Fuzzy-Model based nonlinear networked control systems,” IEEE Transactions on Industrial Electronics, vol. 63, no. 2, pp. 1207–1217, 2016. [click]CrossRefGoogle Scholar
  8. [8]
    J. Qiu, S. X. Ding, H. Gao, and S. Yin, “Fuzzy model based reliable static output feedback H control of nonlinear hyperbolic PDE systems,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 2, pp. 388–400, 2016. [click]CrossRefGoogle Scholar
  9. [9]
    A. Abe, “Anti-sway control for overhead cranes using neural networks,” International Journal of Innovative Computing Information and Control, vol. 7, no. 7, pp. 4251–4262, 2011.Google Scholar
  10. [10]
    J. Jafari, M. Ghazal, and M. Nazemizadeh, “A LQR optimal method to control the position of an overhead crane,” International Journal of Robotics and Automation, vol. 3, no. 4, pp. 252–258, 2014. [click]Google Scholar
  11. [11]
    H. Saeidi, M. Naraghi, and A. Asadollah, “A neural network self tuner based on input shapers behavior for anti sway system of gantry cranes,” Journal of Vibration and Control, vol. 19, no. 13, pp. 1936–1949, 2012.CrossRefGoogle Scholar
  12. [12]
    X. Shi and T. P. Zhang, “Sliding mode controls of doublependulum crane systems,” Journal of Mechanical Sciences and Technology, vol. 27, no. 6, pp. 1863–1873, 2013. [click]CrossRefGoogle Scholar
  13. [13]
    K. Huang and S. Zuo, “Neural network-based sliding mode control for permanent magnet synchronous motor,” The Open Electrical & Electronic Engineering Journal, vol. 5, no. 9, pp. 314–320, 2015. [click]CrossRefGoogle Scholar
  14. [14]
    D. Liu, J. Yi, D. Zhao, and W. Wang, “Swing-free transporting of two-dimensional overhead crane using sliding mode fuzzy control,” Proc. of American Control Conference, Boston, vol. 2, pp. 1764–1769, 2004.Google Scholar
  15. [15]
    S. Frikha, M. Djemel, and N. Derbel, “Observer based adaptive neuro-sliding mode control for MIMO nonlinear systems,” International Journal of Control, Automation and Systems, vol. 8, no. 2, pp. 250–256, 2010. [click]CrossRefMATHGoogle Scholar
  16. [16]
    W. Chen, R. Li, and L. Jing, “Adaptive sliding mode control of container cranes,” IET Control Theory and Applications, vol. 6, no. 5, pp. 662–668, 2012. [click]MathSciNetCrossRefGoogle Scholar
  17. [17]
    K. Choi and J. S. Lee, “Sliding mode control of overhead crane,” International Journal of Modeling and Simulation, vol. 31, pp. 203–209, 2011.Google Scholar
  18. [18]
    Y. Tao, J. Zheng, and Y. Lin, “A sliding mode control-based on a RBF neural network for deburring industry robotic systems,” International Journal of Advanced Robotic Systems, vol. 16, no. 8 pp. 1–10, 2016.Google Scholar
  19. [19]
    S. Sefriti, J. Boumhidi, R. Naoual, and I. Boumhidi, “Adaptive neural network sliding mode control for electricallydriven robot manipulators,” Journal of Control Engineering and Applied Informatics, vol. 14, no. 4, pp. 27–32, 2012.Google Scholar
  20. [20]
    L. Yugang and L. Yamgmin, “Dynamic modeling and adaptive neural-fuzzy control for nonholonomic mobile manipulators moving on a slope,” International Journal of Control, Automation, and Systems, vol. 4, no. 2, pp. 197–203, 2006.Google Scholar
  21. [21]
    W. He and S. Ge, “Cooperative control of a non uniform gantry crane with constrained tension,” Automatica, vol. 66, pp. 146–154, 2016. [click]MathSciNetCrossRefMATHGoogle Scholar
  22. [22]
    W. He, S. Zhang, Y. Ge, and Y. Li, “Adaptive control of a flexible crane system with the boundary output constraint,” IEEE Transactions on Industrial Electronics, vol. 61, no. 8, pp. 4126–4133, 2014. [click]CrossRefGoogle Scholar
  23. [23]
    J. Yu, Z. Peng, L. Cai, and B. Wu, “Adaptive control design for high-order MIMO nonlinear time-delay systems based on neural networks,” Scientific Journal of Control Engineering, vol. 4, no. 2, pp. 43–50, 2014.Google Scholar
  24. [24]
    D. Liu and L. Muguo, “Adaptive wavelet neural network backstepping sliding mode tracking control for PMSM drive system,” Automatica, vol. 55, no. 4, pp. 405–415, 2014.Google Scholar
  25. [25]
    S. Gao, J. Liu, Y. Li, K. Hong, and Y. Zhang, “Dual-layer fuzzy control architecture for the case rover arm,” International Journal of Control, Automation and Systems, vol. 13, no. 5, pp. 1262–1271, 2015. [click]CrossRefGoogle Scholar
  26. [26]
    H. Wong and A. Rad, “State observer based indirect adaptive fuzzy tracking control,” Simulation Modeling Practice and Theory, vol. 13, pp. 646–663, 2005. [click]CrossRefGoogle Scholar
  27. [27]
    Z. Du and T. Lin, “Adaptive fuzzy tracking control for MIMO uncertain nonlinear time-delay systems,” International Journal of Advancements in Computing Technology, vol. 3, no. 6, pp. 10–20, 2011.CrossRefGoogle Scholar
  28. [28]
    Y. Liu and Y. Li, “Sliding mode adaptive neural networks control for nonholonomic mobile modular manipulators,” Journal of Intelligent and Robotics Systems, vol. 44, no. 3, pp. 203–224, 2005. [click]CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Control and Energy Management LaboratorySfax UniversitySfaxTunisia

Personalised recommendations