Skip to main content
Log in

Interval estimation and optimization for motion trajectory of overhead crane under uncertainty

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

The parameter uncertainty has an important effect on the motion planning of overhead cranes, especially in relation to its industrial safety of production activities. Thus, a novel uncertain estimation-and-optimization strategy is proposed for motion planning of overhead cranes with uncertainty in this paper. The main work of this paper includes the following aspects. First, the overhead crane is simplified as a double pendulum model and the corresponding motion planning is described as an optimal control problem with uncertainty. Second, uncertainties are expressed as interval parameters where only the upper and lower bounds are required without probability information and a bounds estimation problem for optimal control with uncertainty is established; the solution contains all possible values. Third, the bounds estimation problem is solved by a surrogate model-based method, the motion trajectory intervals of overhead cranes are obtained. Fourth, in order to reduce the influence of uncertainty on the motion planning of overhead cranes, an optimization method is introduced to reduce the sensitivity to uncertainty. Finally, the numerical examples show that high accurate interval estimation results are obtained with a reasonable computational cost, and the sensitivity of motion trajectory to uncertainty is reduced obviously with the help of optimization. The proposed strategy provides a guidance for uncertain analysis and online controller design of overhead cranes.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Vaughan, J., Kim, D., Singhose, W.: Control of tower cranes with double-pendulum payload dynamics. IEEE Trans. Control Syst. Technol. 18(6), 1345–1358 (2010)

    Google Scholar 

  2. Liu, Y., Yu, H.: A survey of underactuated mechanical systems. IET Control Theory Appl. 7(7), 921–935 (2013)

    Article  MathSciNet  Google Scholar 

  3. Fang, Y., Ma, B., Wang, P.: A motion planning-based adaptive control method for an underactuated crane system. IEEE Trans. Control Syst. Technol. 20(1), 241–248 (2012)

    Google Scholar 

  4. Abdel-Rarman, E., Nayfeh, A., Masoud, Z.: Dynamics and control of cranes: a review. J. Vib. Control 9(7), 863–908 (2003)

    MATH  Google Scholar 

  5. Zhang, X., Fang, Y.: Minimum-time trajectory planning for underactuated overhead crane systems with state and control constraints. IEEE Trans. Ind. Electron. 61(12), 6915–6925 (2014)

    Article  Google Scholar 

  6. Piazzi, A., Visioli, A.: Optimal dynamic-inversion-based control of an overhead crane. IEE Proc. Control Theory Appl. 149(5), 405–411 (2002)

    Article  Google Scholar 

  7. Moon, M.S., VanLandingham, H.F., Beliveau, Y.J.: Fuzzy time optimal control of crane load. In: Proceedings of the 35th IEEE Conference on Decision and Control, pp. 1127–1132 (1996)

  8. Wu, Z., Xia, X.: Optimal motion planning for overhead cranes. IET Control Theory Appl. 8(17), 1833–1842 (2014)

    Article  Google Scholar 

  9. Chen, H., Fang, Y., Sun, N.: A time-optimal trajectory planning strategy for double pendulum cranes with swing suppression. In: Proceedings of the 35th Chinese Control Conference, pp. 4599–4604, Chengdu (2016)

  10. Chen, H., Fang, Y., Sun, N.: A swing constrained time-optimal trajectory planning strategy for double pendulum crane systems. Nonlinear Dyn. 89(2), 1513–1524 (2017)

    Article  MathSciNet  Google Scholar 

  11. Wu, Z., Xia, X.: Energy efficiency of overhead cranes. In: 19th World Congress of the International-Federation-of-Automatic-Control, vol. 47(3), pp. 19–24, Cape Town (2014)

  12. Zhang, M., Ma, X.: A partially saturated adaptive learning controller for overhead cranes with payload hoisting/lowering and unknown parameters. Nonlinear Dyn. 89(3), 1779–1791 (2017)

    Article  MathSciNet  Google Scholar 

  13. Liu, D., Yi, J., Zhao, D., Wang, W.: Adaptive sliding mode fuzzy control for a two-dimensional overhead crane. Mechatronics 15(5), 505–522 (2015)

    Article  Google Scholar 

  14. Park, M.-S., Chwa, D., Hong, H.-S.: Antisway tracking control of overhead cranes with system uncertainty and actuator nonlinearity using an adaptive fuzzy sliding-mode control. IEEE Trans. Ind. Electron. 55(11), 3972–3984 (2008)

    Article  Google Scholar 

  15. Park, M.-S., Chwa, D., Eom, M.: Adaptive sliding-mode antiswing control of uncertain overhead cranes with highspeed hoisting motion. IEEE Trans. Fuzzy Syst. 22(5), 1262–1271 (2014)

    Article  Google Scholar 

  16. Tuan, L., Lee, S,-G.: Combined control with sliding mode and partial feedback linearization for 3D overhead craned. Int. J. Robust Nonlinear Control 24(18), 3372–3386 (2014)

    Article  MATH  Google Scholar 

  17. Astill, C., Imosseir, S., Shinozuka, M.: Impact loading on structures with random properties. J. Struct. Mech. 1(1), 63–77 (1972)

    Article  Google Scholar 

  18. Xiu, D., Karniadakis, G.: The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2), 619–644 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  19. Sun, T.-C.: A finite element method for random differential equations with random coefficients. SIAM J. Numer. Anal. 16(6), 1019–1035 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  20. Hanss, M.: The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets Syst. 130(3), 277–289 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  21. Chalco-Cano, Y., Román-Flores, H.: Comparation between some approaches to solve fuzzy differential equations. Fuzzy Sets Syst. 160(11), 1517–1527 (2009)

    Article  MATH  Google Scholar 

  22. Nieto, J., Khastan, A., Ivaz, K.: Numerical solution of fuzzy differential equations under generalized differentiability. Nonlinear Anal. Hybrid Syst. 3(4), 700–707 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  23. Ben-Haim, Y., Chen, G., Soong, T.: Maximum structural response using convex models. J. Eng. Mech. ASCE 122(4), 325–333 (1996)

    Article  Google Scholar 

  24. Alefeld, G., Mayer, G.: Interval analysis: theory and applications. J. Comput. Appl. Math. 121(1), 421–464 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  25. Liu, Z., Wang, T., Li, J.: Non-intrusive hybrid interval method for uncertain nonlinear systems using derivative information. Acta Mech. Sin. 32(1), 170–180 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  26. Wu, J., Zhang, Y., Chen, L., Chen, P., Qin, G.: Uncertain analysis of vehicle handling using interval method. Int. J. Veh. Des. 56(1), 81–105 (2011)

    Article  Google Scholar 

  27. Wu, J., Zhang, Y., Chen, L., Luo, Z.: A Chebyshev interval method for nonlinear dynamic systems under uncertainty. Appl. Math. Model. 37(6), 4578–4591 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  28. Wu, J., Luo, Z., Zhang, Y., Zhang, N., Chen, L.: Interval uncertain method for multi-body mechanical systems based on Chebyshev inclusion functions. Int. J. Numer. Methods Eng. 95(7), 608–630 (2013)

    Article  MATH  Google Scholar 

  29. Liang, J., Wu, J., Zhang, Nong, Luo, Z., Zhu, S.: Interval uncertain analysis of active hydraulically interconnected suspension system. Mech. Eng. 8(5), 1–14 (2016)

    Article  Google Scholar 

  30. Wu, J., Luo, Z., Zhang, Y., Zhang, N.: An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels. Appl. Math. Model. 38, 3706–3723 (2014)

    Article  MATH  Google Scholar 

  31. Xia, B., Qin, Y., Yu, D., Jiang, C.: Dynamics response analysis of structure under time-variant interval process model. J. Sound Vib. 381, 121–138 (2016)

    Article  Google Scholar 

  32. Qiu, Z., Ma, L., Wang, X.: Non-probabilistic interval analysis method for dynamic response analysis of nonlinear systems with uncertainty. J. Sound Vib. 319, 531–540 (2009)

    Article  Google Scholar 

  33. Jiang, C., Ni, B., Liu, N., Han, X., Liu, J.: Interval process model and non-random vibration analysis. J. Sound Vib. 373, 104–131 (2016)

    Article  Google Scholar 

  34. Li, Y., Wang, X., Huang, R., Qiu, Z.: Actuator placement robust optimization for vibration control system with interval parameters. Aerosp. Sci. Technol. 45, 88–98 (2015)

    Article  Google Scholar 

  35. Arnold, V.I.: Mathematical Methods of Classical Mechanics. Springer, New York (1989)

    Book  Google Scholar 

  36. Hairer, E., Lubich, C., Wanner, G.: Geometric Numerical Integration: Structure-Preserving Algorithm for Ordinary Differential Equations. Springer, New York (2006)

    MATH  Google Scholar 

  37. Peng, H., Gao, Q., Wu, Z., Zhong, X.: Symplectic approaches for solving two-point boundary-value problems. J. Guid. Control Dyn. 35(2), 653–659 (2012)

    Article  Google Scholar 

  38. Peng, H., Wang, Xi, Li, M.: An hp symplectic pseudospectral method for nonlinear optimal control. Commun. Nonlinear Sci. Numer. Simul. 42, 623–644 (2017)

    Article  MathSciNet  Google Scholar 

  39. Li, M., Peng, H., Zhong, W.: A symplectic sequence iteration approach for nonlinear optimal control problems with state-control constraints. J. Frankl. Inst. Eng. Appl. Math. J. 352(6), 2381–2406 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  40. Peng, H., Wang, X., Zhang, S., Chen, B.: An iterative symplectic pseudospectral method to solve nonlinear state-delayed optimal control problems. Commun. Nonlinear Sci. Numer. Simul. 48, 95–114 (2017)

    Article  MathSciNet  Google Scholar 

  41. Wu, J., Luo, Z., Zhang, N.: A new sampling scheme for developing metamodels with zeros of Chebyshev polynomials. Eng. Optim. 47(9), 1264–1288 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful for the financial support of the National Key Research and Development Program of China (2017YFB1301103) and the National Natural Science Foundation of China (11772074, 11761131005, 91748203, 91648204).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haijun Peng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, H., Shi, B., Wang, X. et al. Interval estimation and optimization for motion trajectory of overhead crane under uncertainty. Nonlinear Dyn 96, 1693–1715 (2019). https://doi.org/10.1007/s11071-019-04879-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-019-04879-w

Keywords

Navigation