Advertisement

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

  • Viet-Anh Le
  • Hai-Xuan Le
  • Linh NguyenEmail author
  • Minh-Xuan Phan
Research Article

Abstract

In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.

Keywords

3D overhead crane adaptive control hierarchical sliding mode control neural network radial basis function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    H. Butler, G. Honderd, J. Van Amerongen. Model reference adaptive control of a gantry crane scale model. IEEE Control Systems Magazine, vol. 11, no. 1, pp. 57–62, 1991. DOI:  https://doi.org/10.1109/37.103358.zbMATHGoogle Scholar
  2. [2]
    Y. Z. Qian, Y. C. Fang, T. Yang. An energy-based nonlinear coupling control for offshore ship-mounted cranes. International Journal of Automation and Computing, vol. 15, no. 5, pp. 570–581, 2018. DOI:  https://doi.org/10.1007/s11633-018-1134-y.Google Scholar
  3. [3]
    J. Yoon, S. Nation, W. Singhose, J. E. Vaughan. Control of crane payloads that bounce during hoisting. IEEE Transactions on Control Systems Technology, vol. 22, no. 3, pp. 1233–1238, 2014. DOI:  https://doi.org/10.1109/TCST.2013.2264288.Google Scholar
  4. [4]
    L. Ramli, Z. Mohamed, A. M. Abdullahi, H. I. Jaafar, I. M. Lazim. Control strategies for crane systems: A comprehensive review. Mechanical Systems and Signal Processing, vol. 95, pp. 1–23, 2017. DOI:  https://doi.org/10.1016/j.ymssp.2017.03.015.Google Scholar
  5. [5]
    F. Panuncio, W. Yu, X. O. Li. Stable neural PID anti-swing control for an overhead crane. In Proceedings of IEEE International Symposium on Intelligent Control, Hyderabad, India, pp. 53–58, 2013. DOI:  https://doi.org/10.1109/ISIC.2013.6658616.Google Scholar
  6. [6]
    H. H. Lee. Motion planning for three-dimensional overhead cranes with high-speed load hoisting. International Journal of Control, vol. 78, no. 12, pp. 875–886, 2005. DOI:  https://doi.org/10.1080/00207170500197571.MathSciNetzbMATHGoogle Scholar
  7. [7]
    N. Sun, T. Yang, H. Chen, Y. C. Fang, Y. Z. Qian. Adaptive anti-swing and positioning control for 4-DOF rotary cranes subject to uncertain/unknown parameters with hardware experiments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, to be published. DOI:  https://doi.org/10.1109/TSMC.2017.2765183.
  8. [8]
    N. Sun, T. Yang, Y. C. Fang, Y. M. Wu, H. Chen. Transportation control of double-pendulum cranes with a nonlinear quasi-PID scheme: Desggn and experiments. IEEE Transactions on Systems, Man, and Cybernetics: Systems, published online. DOI:  https://doi.org/10.1109/TSMC.2018.2871627.
  9. [9]
    H. Saeidi, M. Naraghi, A. A. Raie. A neural network self tuner based on input shapers behavior for anti sway system of gantry cranes. Journal of Vibration and Control, vol. 19, pp. 1936–1949, 2013. DOI:  https://doi.org/10.1177/10775466312453065.Google Scholar
  10. [10]
    H. M. Omar. Control of gantry and tower cranes, Ph. D. dissertation, Virginia Polytechnic Institute and State University, Blacksburg, USA, 2003.Google Scholar
  11. [11]
    H. M. Omar, A. H. Nayfeh. Anti-swing control of gantry and tower cranes using fuzzy and time-delayed feedback with friction compensation. Shock and Vibration, vol. 12, no. 2, pp. 73–89, 2005. DOI:  https://doi.org/10.1155/2005/890127.Google Scholar
  12. [12]
    Y. Fang, W. E. Dixon, D. M. Dawson, E. Zergeroglu. Nonlinear coupling control laws for an underactuated overhead crane system. IEEE/ASME Transactions on Mechatronics, vol. 8, no. 3, pp. 418–423, 2003. DOI:  https://doi.org/10.1109/TMECH.2003.816822.Google Scholar
  13. [13]
    J. Yu, F. L. Lewis, T. Huang. Nonlinear feedback control of a gantry crane. In Proceedings of American Control Conference, IEEE, Seattle, USA, pp. 4310–4315, 1995. DOI:  https://doi.org/10.1109/ACC.1995.532748.Google Scholar
  14. [14]
    H. Park, D. Chwa, K. S. Hong. A feedback linearization control of container cranes: Varying rope length. International Journal of Control, Automation, and Systems, vol. 5, no. 4, pp. 379–387, 2007.Google Scholar
  15. [15]
    T. A. Le, S. G. Lee, V. H. Dang, S. Moon, B. S. Kim. Partial feedback linearization control of a three-dimensional overhead crane. International Journal of Control, Automation and Systems, vol. 11, no. 4, pp. 718–727, 2013. DOI:  https://doi.org/10.1007/s12555-012-9305-z.Google Scholar
  16. [16]
    T. A. Le, G. H. Kim, M. Y. Kim, S. G. Lee. Partial feedback linearization contro of overhead cranes with varying cable lengths. International Journal of Precision Engineering and Manufacturing, vol. 13, no. 4, pp. 501–507, 2012. DOI:  https://doi.org/10.1007/s12541-012-0065-8.Google Scholar
  17. [17]
    S. K. Cho, H. H. Lee. A fuzzy-logic antiswing controller for three-dimensional overhead cranes. ISA Transactions, vol. 41, no. 2, pp. 235–243, 2002. DOI:  https://doi.org/10.1016/S00019-0578(07)60083-4.Google Scholar
  18. [18]
    M. Mahfouf, C. H. Kee, M. F. Abbod, D. A. Linkens. Fuzzy logic-based anti-sway control design for overhead cranes. Neural Computing & Applications, vol. 9, no. 1, pp. 38–43, 2000. DOI:  https://doi.org/10.1007/s005210070033.Google Scholar
  19. [19]
    L. F. Wang, H. B. Zhang, Z. Kong. Anti-swing control of overhead crane based on double fuzzy controllers. In Proceedings of the 27th Chinese Control and Decision Conference, IEEE, Qingdao, China, pp. 981–986, 2015. DOI:  https://doi.org/10.1109/CCDC.2015.7162061.Google Scholar
  20. [20]
    K. K. Shyu, C. L. Jen, L. J. Shang. Design of sliding-mode controller for anti-swing control of overhead cranes. In Proceedings of the 31st Annual Conference of IEEE Industrial Electronics Society, Raleigh, USA, pp. 147–152, 2005. DOI:  https://doi.org/10.1109/IECON.2005.1568895.Google Scholar
  21. [21]
    D. W. Qian, J. Q. Yi, D. B. Zhao. Control of overhead crane systems by combining sliding mode with fuzzy regulator. IFAC Proceedings Volumes, vol. 44, no. 1, pp. 9320–9325, 2011. DOI:  https://doi.org/10.3182/20110828-6-IT-1002.01716.Google Scholar
  22. [22]
    T. A. Le, J. J. Kim, S. G. Lee, T. G. Lim, N. C. Luong. Second-order sliding mode control of a 3D overhead crane with uncertain system parameters. International Journal of Precision Engineering and Manufacturing, vol. 15, no. 5, pp. 811–819, 2014. DOI:  https://doi.org/10.1007/s12541-014-0404-z.Google Scholar
  23. [23]
    Q. H. Ngo, K. S. Hong. Sliding-mode antisway control of an offshore container crane. IEEE/ASME Transactions on Mechatronics, vol. 17, no. 2, pp. 201–209, 2012. DOI:  https://doi.org/10.1109/TMECH.2010.2093907.Google Scholar
  24. [24]
    G. Bartolini, A. Pisano, E. Usai. Second-order sliding-mode control of container cranes. Automatica, vol. 38, no. 10, pp. 1783–1790, 2002. DOI:  https://doi.org/10.1016/S0005-1098(02)00081-X.MathSciNetzbMATHGoogle Scholar
  25. [25]
    S. Mahjoub, F. Mnif, N. Derbel. Second-order sliding mode approaches for the control of a class of underactuated systems. International Journal of Automation and Computing, vol. 12, no. 2, pp. 134–141, 2015. DOI:  https://doi.org/10.1007/s11633-015-0880-3.Google Scholar
  26. [26]
    W. M. Xu, X. Zheng, Y. Q. Liu, M. J. Zhang, Y. Y. Luo. Adaptive dynamic sliding mode control for overhead cranes. In Proceedings of the 34th Chinese Control Conference, IEEE, Hangzhou, China, pp. 3287–3292, 2015. DOI:  https://doi.org/10.1109/ChiCC.2015.7260147.Google Scholar
  27. [27]
    W. Wang, J. Yi, D. Zhao, D. Liu. Design of a stable sliding-mode controller for a class of second-order underactuated systems. IEE Proceedings — Control Theory and Applications, vol. 151, no. 6, pp. 630–690, 2004. DOI:  https://doi.org/10.1049/ip-cta:20040902.Google Scholar
  28. [28]
    D. W. Qian, J. Q. Yi, D. B. Zhao. Hierarchical sliding mode control for a class of SIMO under-actuated systems. Control and Cybernetics, vol. 37, no. 1, pp. 159–175, 2008.MathSciNetzbMATHGoogle Scholar
  29. [29]
    D. W. Qian, J. Q. Yi. Hierarchical Sliding Mode Control for Under-actuated Cranes: Design, Analysis and Simulation, Berlin Heidelberg, Germany: Springer, 2015. DOI:  https://doi.org/10.1007/978-3-662-48417-3.zbMATHGoogle Scholar
  30. [30]
    H. Le Xuan, T. Nguyen Van, A. Le Viet, N. V. T. Thuy, M. P. Xuan. Adaptive backstepping hierarchical sliding mode control for uncertain 3D overhead crane systems. In Proceedings of International Conference on System Science and Engineering, IEEE, Ho Chi Minh City, Vietnam, pp. 438–443, 2017. DOI:  https://doi.org/10.1109/ICSSE.2017.8030913.Google Scholar
  31. [31]
    W. Wang, X. D. Liu, J. Q. Yi. Structure design of two types of sliding-mode controllers for a class of under-actuated mechanical systems. IET Control Theory & Applications, vol 1, no. 1, pp. 163–172, 2007. DOI  https://doi.org/10.1049/ietcta:20050435.MathSciNetGoogle Scholar
  32. [32]
    D. W. Qian, X. J. Liu, J. Q. Yi. Adaptive control based on hierarchical sliding mode for under-actuated systems. In Proceedings of International Conference on Mechatronics and Automation, IEEE, Chengdu, China, pp. 1050–1055, 2012. DOI:  https://doi.org/10.1109/ICMA.2012.6283395.Google Scholar
  33. [33]
    J. H. Yang, K. S. Yang. Adaptive coupling control for overhead crane systems. Mechatronics, vol. 17, no. 2–3, pp. 143–152, 2007. DOI:  https://doi.org/10.1016/j.mechatronics.2006.08.004.Google Scholar
  34. [34]
    L. A. Tuan, S. G. Lee, L. C. Nho, D. H. Kim. Model reference adaptive sliding mode control for three dimensional overhead cranes. International Journal of Precision Engineering and Manufacturing, vol. 14, no. 8, pp. 1329–1338, 2013. DOI:  https://doi.org/10.1007/s12541-013-0180-1.Google Scholar
  35. [35]
    M. S. Park, D. Chwa, S. K. Hong. Antisway tracking control of overhead cranes with system uncertainty and actuator nonlinearity using an adaptive fuzzy sliding-mode control. IEEE Transactions on Industrial Electronics, vol. 55, no. 11, pp. 3972–3984, 2008. DOI:  https://doi.org/10.1109/TIE.2008.2004385.Google Scholar
  36. [36]
    M. S. Park, D. Chwa, M. Eom. Adaptive sliding-mode antisway control of uncertain overhead cranes with highspeed hoisting motion. IEEE Transactions on Fuzzy Systems, vol. 22, no. 5, pp. 1262–1271, 2014. DOI:  https://doi.org/10.1109/TFUZZ.2013.2290139.Google Scholar
  37. [37]
    L. C. Hung, H. Y. Chung. Decoupled control using neural network-based sliding-mode controller for nonlinear systems. Expert Systems with Applications, vol. 32, no. 4, pp. 1168–1182, 2007. DOI:  https://doi.org/10.1016/j.eswa.2006.02.024.Google Scholar
  38. [38]
    G. Ji. Adaptive neural network dynamic surface control for perturbed nonlinear time-delay systems. International Journal of Automation and Computing, vol. 9, no. 2, pp. 135–141, 2012. DOI:  https://doi.org/10.1007/s11633-012-0626-4.Google Scholar
  39. [39]
    L. C. Hung, H. Y. Chung. Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems. International Journal of Approximate Reasoning, vol. 46, no. 1, pp. 74–97, 2007. DOI:  https://doi.org/10.1016/j.ijar.2006.08.002.MathSciNetzbMATHGoogle Scholar
  40. [40]
    C. C. Tsai, H. L. Wu, K. H. Chuang. Intelligent sliding-mode motion control using fuzzy wavelet networks for automatic 3D overhead cranes. In Proceedings of SICE Annual Conference, IEEE, Akita, Japan, pp. 1256–1261, 2012.Google Scholar
  41. [41]
    Y. Tao, J. Q. Zheng, Y. C. Lin. A sliding mode control-based on a RBF neural network for deburring industry robotic systems. International Journal of Advanced Robotic Systems, vol. 13, no. 1, Article number 8, 2016. DOI:  https://doi.org/10.5772/62002. DOI:  https://doi.org/10.5772/62002.
  42. [42]
    S. Mahjoub, F. Mnif, N. Derbel. Radial-basis-functions neural network sliding mode control for underactuated manipulators. In Proceedings of the 10th International Multi-Conferences on Systems, Signals & Devices, IEEE, Hammamet, Tunisia, 2013. DOI:  https://doi.org/10.1109/SSD.2013.6564106.Google Scholar
  43. [43]
    H. C. Lu, C. H. Tsai, M. H. Chang. Radial basis function neural network with sliding mode control for robotic manipulators. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, pp. 1209–1215, 2010. DOI:  https://doi.org/10.1109/ICSMC.2010.5642384.Google Scholar
  44. [44]
    S. Mahjoub, F. Mnif, N. Derbel, M. Hamerlain. Radial-basis-functions neural network sliding mode control for underactuated mechanical systems. International Journal of Dynamics and Control, vol. 2, no. 4, pp. 533–541, 2014. DOI:  https://doi.org/10.1007/s40435-014-0070-0.Google Scholar
  45. [45]
    C. Zhang, A. M. Zhang, H. Zhang, Y. F. Bai, C. J. Guo, Y. S. Geng. RBF neural networks sliding mode controller design for static var compensator. In Proceedings of the 34th Chinese Control Conference, IEEE, Hangzhou, China, pp. 3501–3506, 2015. DOI:  https://doi.org/10.1109/ChiCC.2015.7260179.Google Scholar
  46. [46]
    L. V. Anh, L. X. Hai, V. D. Thuan, P. Van Trieu, L. A. Tuan, H. M. Cuong. Designing an adaptive controller for 3D overhead cranes using hierarchical sliding mode and neural network. In Proceedings of IEEE International Conference on System Science and Engineering, New Taipei, China, pp. 1–6, 2018. DOI:  https://doi.org/10.1109/ICSSE.2018.8520162.Google Scholar
  47. [47]
    H. H. Lee. Modeling and control of a three-dimensional overhead crane. Journal of Dynamic Systems, Measurement, and Control, vol. 120, no. 4, pp. 471–176, 1998. DOI:  https://doi.org/10.1115/1.2801488.Google Scholar
  48. [48]
    R. R. Selmic, F. L. Lewis. Deadzone compensation in motion control systems using neural networks. IEEE Transactions on Automatic Control, vol. 45, no. 4, pp. 602–613, 2000. DOI:  https://doi.org/10.1109/9.847098.MathSciNetzbMATHGoogle Scholar
  49. [49]
    J. Zhou, C. Y. Wen, T. S. Li. Adaptive output feedback control of uncertain nonlinear systems with hysteresis non-linearity. IEEE Transactions on Automatic Control, vol. 57, no. 10, pp. 2627–2633, 2012. DOI:  https://doi.org/10.1109/TAC.2012.2190208.MathSciNetzbMATHGoogle Scholar
  50. [50]
    J. Park, I. W. Sandberg. Universal approximation using radial-basis-function networks. Neural Computation, vol. 3, no. 2, pp. 246–257, 1991. DOI:  https://doi.org/10.1162/neco.1991.3.2.246.Google Scholar
  51. [51]
    M. T. Hagan, H. B. Demuth, M. H. Beale, O. De Jess. Neural Network Design, 2nd ed., Pittsburgh, USA: Martin Hagan, 2014.Google Scholar
  52. [52]
    W. Z. Gao, R. R. Selmic. Neural network control of a class of nonlinear systems with actuator saturation. IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 147–156, 2006. DOI:  https://doi.org/10.1109/TNN.2005.863416.Google Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Automatic ControlHanoi University of Science and TechnologyHanoiVietnam
  2. 2.Centre for Autonomous SystemsUniversity of Technology SydneyUltimoAustralia

Personalised recommendations