Lead-Lag Controller-Based Iterative Learning Control Algorithms for 3D Crane Systems

  • Radu-Emil PrecupEmail author
  • Florin-Cristian Enache
  • Mircea-Bogdan Rădac
  • Emil M. Petriu
  • Stefan Preitl
  • Claudia-Adina Dragoş
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 2)


This chapter deals with the application of two Iterative Learning Control (ILC) structures to the position control of 3D crane systems. The control system structures are based on Cascade Learning (CL) and Previous and Current Cycle Learning (PCCL) which improve the control system performance with frequency domain designed lead-lag controllers for the x-axis and for the y-axis. The parameters of continuous-time real PD learning rules which are also implemented in real-world applications as lead-lag controllers are set such that to fulfill the convergence conditions of CL and PCCL. Elements of anti-swing control for the PCCL structure are discussed. Experimental results are given to solve the crane position control problem of a 3D crane system laboratory equipment.


Iterative Learn Control Overhead Crane Crane System Cuckoo Optimization Algorithm Control System Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Toxqui, R., Yu, W.: Anti-swing control for overhead crane with neural compensation. In: Proceedings of 2006 International Joint Conference on Neural Networks (IJCNN 2006), Vancouver, BC, Canada, pp. 9447–9453 (2006)Google Scholar
  2. 2.
    Yu, W., Li, X., Irwin, G.W.: Stable anti-swing control for an overhead crane with velocity estimation and fuzzy compensation. In: Lowen, R., Verschoren, A. (eds.) Foundations of Generic Optimization, Applications of Fuzzy Control, Genetic Algorithms and Neural Networks, vol. 2, pp. 223–240. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Yu, W., Li, X.: Anti-swing control for an overhead crane with intelligent compensation. In: Proceedings of 3rd International Symposium on Resilient Control Systems (ISRCS 2010), Idaho Falls, ID, USA, pp. 85–90 (2010)Google Scholar
  4. 4.
    Yoshida, Y., Tabata, H.: Visual feedback control of an overhead crane and its combination with time-optimal control. In: Proceedings of 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2008), Xi’an, China, pp. 1114–1119 (2008)Google Scholar
  5. 5.
    Westerberg, S., Manchester, I.R., Mettin, U., La Hera, P., Shiriaev, A.: Virtual environment teleoperation of a hydraulic forestry crane. In: Proceedings of 2008 IEEE International Conference on Robotics and Automation (ICRA 2008), Pasadena, CA, USA, pp. 4049–4054 (2008)Google Scholar
  6. 6.
    Chang, C.Y., Chiang, K.H.: The nonlinear 3-D crane control with an intelligent operating method. In: Proceedings of 2008 SICE Annual Conference, Tokyo, Japan, pp. 2917–2921 (2008)Google Scholar
  7. 7.
    Chwa, D.: Nonlinear tracking control of 3-D overhead cranes against the initial swing angle and the variation of payload weight. IEEE Trans Contr. Syst. Technol. 17, 876–883 (2009)CrossRefGoogle Scholar
  8. 8.
    Ahmad, M.A., Ismail, R.M.T.R., Ramli, M.S.: Input shaping techniques for anti-sway control of a 3-D gantry crane system. In: Proceedings of 2009 International Conference on Mechatronics and Automation (ICMA 2009), Changchun, China, pp. 2876–2881 (2009)Google Scholar
  9. 9.
    Ahmad, M.A., Ismail, R.M.T.R., Ramli, M.S., Abd Ghani, N.M., Hambali, N.: Investigations of feed-forward techniques for anti-sway control of 3-D gantry crane system. In: Proceedings of 2009 IEEE Symposium on Industrial Electronics & Applications (ISIEA 2009), Kuala Lumpur, Malaysia, vol. 1, pp. 265–270 (2009)Google Scholar
  10. 10.
    Kaneshige, A., Miyoshi, T., Terashima, K.: The development of an autonomous mobile overhead crane system for the liquid tank transfer. In: Proceedings of 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2009), Singapore, pp. 630–635 (2009)Google Scholar
  11. 11.
    Pisano, A., Scodina, S., Usai, E.: Load swing suppression in the 3-dimensional overhead crane via second-order sliding-modes. In: Proceedings of 11th International Workshop on Variable Structure Systems (VSS 2010), Mexico City, Mexico, pp. 452–457 (2010)Google Scholar
  12. 12.
    Cuenca, Á., Salt, J., Sala, A., Piza, R.: A delay-dependent dual-rate PID controller over an Ethernet network. IEEE Trans. Ind. Informat. 7, 18–29 (2011)CrossRefGoogle Scholar
  13. 13.
    Yu, W., Moreno-Armendariz, M.A., Ortiz Rodriguez, F.: Stable adaptive compensation with fuzzy CMAC for an overhead crane. Inf. Sci. 181, 4895–4907 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jovanović, Z., Antić, D., Stajić, Z., Milošević, M., Nikolić, S., Perić, S.: Genetic algorithms applied in parameters determination of the 3D crane model. Facta Universitatis, Series: Automatic Control and Robotics 10, 19–27 (2011)Google Scholar
  15. 15.
    Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control. IEEE Control Syst. Mag. 26, 96–114 (2006)CrossRefGoogle Scholar
  16. 16.
    Ahn, H.S., Moore, K.L., Chen, Y.: Iterative learning control. Robustness and monotonic convergence for interval systems. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  17. 17.
    Xu, J.X., Panda, S.K., Lee, T.H.: Real-time iterative learning control. Design and applications. Springer, Heidelberg (2009)zbMATHGoogle Scholar
  18. 18.
    Owens, D.H., Hätönen, J.: Iterative learning control - An optimization paradigm. Annu. Rev. Control 29, 57–70 (2005)CrossRefGoogle Scholar
  19. 19.
    Abidi, K., Xu, J.X.: Iterative learning control for sampled-data systems: From theory to practice. IEEE Trans. Ind. Electron. 58, 3002–3015 (2011)CrossRefGoogle Scholar
  20. 20.
    Wang, Y., Gao, F., Doyle, F.: Survey on iterative learning control, repetitive control, and run-to-run control. J. Process Contr. 19, 1589–1600 (2009)CrossRefGoogle Scholar
  21. 21.
    Ruan, X., Bien, Z., Park, K.H.: Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking. IEEE Trans. Syst. Man Cybern. A Syst. Humans 38, 238–252 (2008)CrossRefGoogle Scholar
  22. 22.
    Liu, T., Gao, F.: Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties. Chem. Eng. Sci. 66, 6134–6144 (2010)Google Scholar
  23. 23.
    Tan, K., Zhao, S., Xu, J.X.: Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach. IET Contr. Theory Appl. 1, 90–96 (2007)CrossRefGoogle Scholar
  24. 24.
    Wu, J., Ding, H.: Reference adjustment for a high-acceleration and high-precision platform via A-type of iterative learning control. Proc. IMechE I J. Syst. Control Eng. 221, 781–789 (2007)CrossRefGoogle Scholar
  25. 25.
    Precup, R.E., Enache, F.C., Rădac, M.B., Petriu, E.M., Dragoş, C.A., Preitl, S.: Iterative learning control application to a 3D crane system. In: Proceedings of 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2011), Noordwijkerhout, The Netherlands, vol. 1, pp. 117–122 (2011)Google Scholar
  26. 26.
    Rădac, M.B., Enache, F.C., Precup, R.E., Petriu, E.M., Preitl, S., Dragoş, C.A.: Previous and current cycle learning approach to a 3D crane system laboratory equipment. In: Proceedings of 15th International Conference on Intelligent Engineering Systems (INES 2011), Poprad, Slovakia, pp. 197–202 (2011)Google Scholar
  27. 27.
    Inteco Ltd., 3D crane, user’s manual. Inteco Ltd., Krakow, Poland (2008)Google Scholar
  28. 28.
    Chen, W., Wu, Q., Tafazzoli, E., Saif, M.: Actuator fault diagnosis using high-order sliding mode differentiator (HOSMD) and its application to a laboratory 3D crane. In: Proceedings of 17th World Congress of the International Federation of Automatic Control, Seoul, Korea, pp. 4809–4814 (2008)Google Scholar
  29. 29.
    Enache, F.C.: Iterative learning control-based control solutions. Applications to a 3D crane laboratory equipment. B.Sc. Thesis, Department of Automation and Applied Informatics, “Politehnica” University of Timisoara, Timisoara, Romania (2010)Google Scholar
  30. 30.
    Paláncz, B., Benyó, Z., Kovács, L.: Control system professional suite. IEEE Control Syst. Mag. 25, 67–75 (2005)CrossRefGoogle Scholar
  31. 31.
    Harmati, I., Lantos, B., Payandeh, S.: Fitted stratified manipulation with decomposed path planning on submanifolds. Int. J. Robot. Autom. 20, 135–144 (2005)Google Scholar
  32. 32.
    Vaščák, J.: Navigation of mobile robots using potential fields and computational intelligence means. Acta Polytechnica Hungarica 4, 63–74 (2007)Google Scholar
  33. 33.
    Blažič, S., Matko, D., Škrjanc, I.: Adaptive law with a new leakage term. IET Control Theory Appl. 4, 1533–1542 (2010)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps - a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)Google Scholar
  35. 35.
    Garcia, A., Luviano-Juarez, A., Chairez, I., Poznyak, A., Poznyak, T.: Projectional dynamic neural network identifier for chaotic systems: Application to Chua’s circuit. Int. J. Artif. Intell. 6, 1–18 (2011)Google Scholar
  36. 36.
    Linda, O., Manic, M.: Uncertainty-robust design of interval type-2 fuzzy logic controller for delta parallel robot. IEEE Trans. Ind. Informat. 7, 661–670 (2011)CrossRefGoogle Scholar
  37. 37.
    Baranyi, P., Kóczy, L.T.: A general and specialised solid cutting method for fuzzy rule interpolation. J. Busefal 66, 13–22Google Scholar
  38. 38.
    Baranyi, P., Yam, Y., Várkonyi-Kóczy, A., Patton, R.J.: SVD based reduction to MISO TS fuzzy models. IEEE Trans. Ind. Electron. 50, 232–242 (2003)CrossRefGoogle Scholar
  39. 39.
    Horváth, L., Rudas, I.J.: Modelling and solving methods for engineers. Elsevier, Academic Press, Burlington (2004)Google Scholar
  40. 40.
    Škrjanc, I., Blažič, S., Agamennoni, O.E.: Identification of dynamical systems with a robust interval fuzzy model. Automatica 41, 327–332 (2005)zbMATHCrossRefGoogle Scholar
  41. 41.
    Johanyák, Z.C., Kovács, S.: Fuzzy rule interpolation based on polar cuts. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, pp. 499–511. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  42. 42.
    Wilamowski, B.M., Cotton, N.J., Kaynak, O., Dundar, G.: Computing gradient vector and Jacobian matrix in arbitrarily connected neural networks. IEEE Trans. Ind. Electron. 55, 3784–3790 (2008)CrossRefGoogle Scholar
  43. 43.
    Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Evolving classification of agents’ behaviors: a general approach. Evolving Syst. 1, 161–171 (2010)CrossRefGoogle Scholar
  44. 44.
    Johanyák, Z.C.: Student evaluation based on fuzzy rule interpolation. Int. J. Artif. Intell. 5, 37–55 (2010)Google Scholar
  45. 45.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft. Comp. 11, 5508–5518 (2011)CrossRefGoogle Scholar
  46. 46.
    Alfi, A., Fateh, M.M.: Intelligent identification and control using improved fuzzy particle swarm optimization. Expert Syst. Appl. 38, 12312–12317 (2011)CrossRefGoogle Scholar
  47. 47.
    Kasabov, N., Hamed, H.N.A.: Quantum-inspired particle swarm optimisation for integrated feature and parameter optimisation of evolving spiking neural networks. Int. J. Artif. Intell. 7, 114–124 (2011)Google Scholar
  48. 48.
    Madarász, L., Živčák, J.: Application of medical thermography in the diagnostics of carpal tunnel syndrome. In: Proceedings of IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI 2011), Budapest, Hungary, pp. 535–539 (2011)Google Scholar
  49. 49.
    Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Radu-Emil Precup
    • 1
    Email author
  • Florin-Cristian Enache
    • 1
  • Mircea-Bogdan Rădac
    • 1
  • Emil M. Petriu
    • 2
  • Stefan Preitl
    • 1
  • Claudia-Adina Dragoş
    • 1
  1. 1.Department of Automation and Applied Informatics“Politehnica” University of TimisoaraTimisoaraRomania
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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