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Application of intelligent sliding mode control with moving sliding surface for overhead cranes

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Abstract

In this paper, neural-based fuzzy logic sliding mode control with moving sliding surface has been designed for supervision of an overhead crane. A mathematical model has been established of the crane, and equations of motion have been obtained. First, the suitable sliding surface coefficient has been determined for the fixed sliding surface in the design of sliding mode control. The sliding surface has been moved by using neural-based fuzzy logic algorithm to eliminate disadvantage of the regular sliding mode control. By application of this control algorithm, the control performance incredibly increased. In the application, during the carriage of the load to a target which was 1 m away by a crane with 3 kg of load and 100 cm of rope length, the parameters of effected controllers were updated and their training was realized. In order to display the insensitiveness of the controller to parametric uncertainty, the value of the load was taken as 8 kg and the length of the rope was taken as 3 m and controls for a different target were realized. MATLAB program was used for numerical solutions, and results were examined graphically. Obtained results displayed the success of the algorithm of neural-based fuzzy logic sliding mode control.

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Abbreviations

a, c :

Parameters of the membership functions

b :

Damping coefficient which affects the circular movement of the load

B :

Damping coefficient which affects the linear movement of the car

C :

Slope of sliding surface

g :

Gravity acceleration

L :

Length of the rope

m :

Mass of the load that was transported

M :

Mass of the car

S :

Function of sliding surface

u :

Control force

x :

Linear position of the car

V :

Lyapunov function

z :

Study state variable

μ:

Crisp degrees

ϴ :

Oscillation angle of the load

References

  1. Corriga G, Giua A, Usai G (1998) An implicit gain-scheduling controller for cranes. IEEE Trans Control Syst Technol 6(1):15–20

    Article  Google Scholar 

  2. Chang C (2006) The switching algorithm for the control of overhead crane. Neural Comput Appl 15:350–358

    Article  Google Scholar 

  3. Marek H, Juraj R (2006) Robust crane control. Acta polytechnica hungarica 3(2):91–101

    Google Scholar 

  4. Abdel-Rahman EM, Nayfeh AH, Masoud ZN (2003) Dynamics and control of cranes; a review. J Vib Control 9(7):863–908

    Article  MATH  Google Scholar 

  5. Chang C, Chiang K (2008) Fuzzy projection control law and its application to the overhead crane. Mechatronics 18:607–615

    Article  Google Scholar 

  6. Huang Y, Liang C, Yang Y (2009) The optimum route problem by genetic algorithm for loading/unloading of yard crane. Comput Ind Eng 56:993–1001

    Article  Google Scholar 

  7. Neupert J, Arnold E, Schneider K, Sawodny O (2010) Tracking and anti-sway control for boom cranes. Control Eng Pract 18:31–44

    Article  Google Scholar 

  8. Cho S, Lee H (2002) A fuzzy-logic anti swing controller for three-dimensional overhead cranes. ISA Trans 41:235–243

    Article  Google Scholar 

  9. Sorensen K, Singhose W, Dickerson S (2007) A controller enabling precise positioning and sway reduction in bridge and gantry cranes. Control Eng Pract 15:825–837

    Article  Google Scholar 

  10. Liu D, Yi J, Zhao D, Wang W (2005) Adaptive sliding mode fuzzy control for a two-dimensional overhead crane. Mechatronics 15:505–522

    Article  Google Scholar 

  11. Chen Y, Wang W, Chang C (2009) Guaranteed cost control for an overhead crane with practical constraints: fuzzy descriptor system approach. Eng Appl Artif Intell 22:639–645

    Article  Google Scholar 

  12. Yi J, Yubazaki N, Hirota K (2003) Anti-swing and positioning control of overhead travelling crane. Inf Sci 155:19–42

    Article  Google Scholar 

  13. Mahfouf M, Kee C, Abbod M, Linkens D (2000) Fuzzy logic-based anti-sway control design for overhead cranes. Neural Comput Appl 9:38–43

    Article  Google Scholar 

  14. Yakut O, Alli H (2011) Neural based sliding-mode control with moving sliding surface for the seismic isolation of structures. J Vib Control 17(14):2103–2116

    Article  Google Scholar 

  15. Hung L, Chung H (2007) Decoupled control using neural network-based sliding-mode controller for nonlinear systems. Expert Syst Appl 32:1168–1182

    Article  Google Scholar 

  16. Lin S, Chen Y (1997) Design of self-learning fuzzy sliding mode controllers based on genetic algorithms. Fuzzy Sets Syst 86:139–153

    Article  MATH  Google Scholar 

  17. Slotine FFE, Li W (1991) Applied nonlinear control. Prentice Hall, New Jersey

    MATH  Google Scholar 

  18. Ha Q, Rye D, Durrant H (1999) Fuzzy moving sliding mode control with application to robotic manipulators. Automatica 35:607–616

    Article  MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Firat University, 23119, Elazig, Turkey

    Oguz Yakut

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  1. Oguz Yakut
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Correspondence to Oguz Yakut.

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Yakut, O. Application of intelligent sliding mode control with moving sliding surface for overhead cranes. Neural Comput & Applic 24, 1369–1379 (2014). https://doi.org/10.1007/s00521-013-1351-9

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  • DOI: https://doi.org/10.1007/s00521-013-1351-9

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