Abstract
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.
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References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control. IEEE Control Syst. Mag. 26, 96–114 (2006)
Ahn, H.S., Moore, K.L., Chen, Y.: Iterative learning control. Robustness and monotonic convergence for interval systems. Springer, Heidelberg (2007)
Xu, J.X., Panda, S.K., Lee, T.H.: Real-time iterative learning control. Design and applications. Springer, Heidelberg (2009)
Owens, D.H., Hätönen, J.: Iterative learning control - An optimization paradigm. Annu. Rev. Control 29, 57–70 (2005)
Abidi, K., Xu, J.X.: Iterative learning control for sampled-data systems: From theory to practice. IEEE Trans. Ind. Electron. 58, 3002–3015 (2011)
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)
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)
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)
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)
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)
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)
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)
Inteco Ltd., 3D crane, user’s manual. Inteco Ltd., Krakow, Poland (2008)
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)
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)
Paláncz, B., Benyó, Z., Kovács, L.: Control system professional suite. IEEE Control Syst. Mag. 25, 67–75 (2005)
Harmati, I., Lantos, B., Payandeh, S.: Fitted stratified manipulation with decomposed path planning on submanifolds. Int. J. Robot. Autom. 20, 135–144 (2005)
Vaščák, J.: Navigation of mobile robots using potential fields and computational intelligence means. Acta Polytechnica Hungarica 4, 63–74 (2007)
Blažič, S., Matko, D., Škrjanc, I.: Adaptive law with a new leakage term. IET Control Theory Appl. 4, 1533–1542 (2010)
Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps - a comparison study. Acta Polytechnica Hungarica 7, 109–122 (2010)
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)
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)
Baranyi, P., Kóczy, L.T.: A general and specialised solid cutting method for fuzzy rule interpolation. J. Busefal 66, 13–22
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)
Horváth, L., Rudas, I.J.: Modelling and solving methods for engineers. Elsevier, Academic Press, Burlington (2004)
Škrjanc, I., Blažič, S., Agamennoni, O.E.: Identification of dynamical systems with a robust interval fuzzy model. Automatica 41, 327–332 (2005)
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)
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)
Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Evolving classification of agents’ behaviors: a general approach. Evolving Syst. 1, 161–171 (2010)
Johanyák, Z.C.: Student evaluation based on fuzzy rule interpolation. Int. J. Artif. Intell. 5, 37–55 (2010)
Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft. Comp. 11, 5508–5518 (2011)
Alfi, A., Fateh, M.M.: Intelligent identification and control using improved fuzzy particle swarm optimization. Expert Syst. Appl. 38, 12312–12317 (2011)
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)
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)
Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)
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Precup, RE., Enache, FC., Rădac, MB., Petriu, E.M., Preitl, S., Dragoş, CA. (2013). Lead-Lag Controller-Based Iterative Learning Control Algorithms for 3D Crane Systems. In: Madarász, L., Živčák, J. (eds) Aspects of Computational Intelligence: Theory and Applications. Topics in Intelligent Engineering and Informatics, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30668-6_2
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DOI: https://doi.org/10.1007/978-3-642-30668-6_2
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