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Abstrakty
In many manufacturing segments, container terminals and shipping yards the automation of material handling systems is an important element of enhancing productivity, safety and efficiency. The fast, precise and safe transfer of goods in crane operations requires a control application solving the problems, including non-collision trajectory planning and limitation of payload oscillations. The paper presents the interval arithmetic-based method of designing a discrete-time closed-loop anti-sway crane control system based on the fuzzy interpolation of linear controller parameters. The interval analysis of a closed-loop control system characteristic polynomial coefficients deviation from their nominal values is proposed to define a minimum number of fuzzy sets on the scheduling variables universe of discourse and to determine the distribution of triangular-shaped membership functions parameters, which satisfy the acceptable range of performances deterioration in the presence of the system’s parameters variation. The effectiveness of this method was proved in experiments conducted using the PAC system on the laboratory scaled overhead crane.
Rocznik
Tom
Strony
863--870
Opis fizyczny
Bibliogr. 40 poz., rys., wykr., alg.
Twórcy
autor
- AGH University of Science and Technology, Faculty of Mechanical Engineering and Robotics, 30 Mickiewicza Ave., 30-059 Cracow, Poland
Bibliografia
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- [3] E.M. Abdel-Rahman, A.H. Nayfeh, and Z.N. Masoud, “Dynamics and control of cranes: A review”, J. Vibration and Control 9, 863-908 (2003).
- [4] P. Hyla, “The crane control systems: a survey”, 17th IFAC Int. Conf. on Methods and Models in Automation and Robotics MMAR 1, 505-509 (2012).
- [5] Y. Sakawa and Y. Shindo, “Optimal control of container cranes”, Automatica 18 (3), 257-266 (1982).
- [6] J.W. Auernig and H. Troger, “Time optimal control of overhead cranes with hoisting of the load”, Automatica 23 (4), 437-447 (1987).
- [7] Y. Fang, B. Ma, P. Wang, and X. Zhang, “A motion planningbased adaptive control method for an underactuated crane system”, IEEE Trans. on Control Systems Technology 20 (1), 241-248 (2012).
- [8] J. Neupert, E. Arnold, K. Schneider, and O. Sawodny, “Tracking and anti-sway control for boom cranes”, Control Engineering Practice 18, 31-44 (2010).
- [9] A. Nowacka-Leverton, M. Michałek, D. Pazderski, and A. Bartoszewicz, “Experimental verification of SMC with moving switching lines applied to hoisting crane vertical motion control”, ISA Transactions 51 (6), 682-693 (2012).
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- [11] M. Mahfouf, C.H. Kee, M.F. Abbod and D.A. Linkens, “Fuzzy logic-based anti-sway control design for overhead cranes”, Neural Computing and Applications (9), 38-43 (2000).
- [12] C.-Y. Chang, “The switching algorithm for the control of overhead crane”, Neural Computing and Applications 15 (3-4), 350-358 (2006).
- [13] X. Li and W. Yu, “Anti-swing control for an overhead crane with fuzzy compensation”, Intelligent Automation and Soft Computing 17 (X), 1-11 (2004).
- [14] M.I. Solihin, J. Wahyudi, and A. Legowo, “Fuzzy-tuned antiswing control of automatic gantry crane”, J. Vibration and Control 16 (1), 127-145 (2010).
- [15] D. Liu, J. Yi, D. Zhao, and W. Wang, “Adaptive sliding mode fuzzy control for a two-dimensional overhead crane”, Mechatronics 15 (5), 505-522 (2005).
- [16] S.-K. Oh, W. Pedrycz, S.-B. Rho, and T.-C. Ahn, “Parameter estimation of fuzzy controller and its application to inverted pendulum”, Engineering Applications of Artificial Intelligence 17 (1), 37-60 (2004).
- [17] N. Sadati and A. Hooshmand, “Design of a gain-scheduling anti-sway controller for tower cranes using fuzzy clustering techniques”, Int. Conf. on Computational Intelligence for Modeling, Control and Automation 1, CD-ROM (2006).
- [18] J. Smoczek and J. Szpytko, “A mechatronics approach in intelligent control systems of the overhead traveling cranes prototyping”, Information Technology and Control 37 (2), 154-158 (2008).
- [19] M.B. Trabia, J.M. Renno and K.A.F. Moustafa, “Generalized design of an anti-swing fuzzy logic controller for an overhead crane with hoist”, J. Vibration and Control 14(3), 319-346 (2008).
- [20] C.-Y. Chang, “Adaptive fuzzy controller of the overhead crane with nonlinear disturbances”, IEEE Trans. on Industrial Informatics 3 (2), 164-172 (2007).
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- [23] J. Smoczek and J. Szpytko, “Design of gain scheduling antisway controller using genetic fuzzy system”, 17th IFAC Int.Conf. on Methods and Models in Automation and Robotics MMAR 1, 573-578 (2012).
- [24] W. Yu, A. Moreno-Wrmendariz, and F.O. Rodriguez, “Stable adaptive compensation with fuzzy CMAC for an overhead crane”, Information Sciences 181 (21), 4895-4907 (2011).
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- [30] Y.C. Soh, R.J. Evans, I.R. Petersen, and R.E. Betz, “Robust pole assignment”, Automatica 23 (5), 601-610 (1987).
- [31] L. Kolev and D. Penev, “Design of control systems under interval uncertainties”, Electrical and Electronics Engineering 2–3, 63–68 (2004).
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- [33] M. Busłowicz, “Robust stability of the new general 2D model of a class of continuous-discrete linear systems”, Bull. Pol. Ac.: Tech. 58 (4), 561–565 (2010).
- [34] B.M. Patre and P.J. Deore, “Robust state feedback for interval systems: an interval analysis approach”, Reliable Computing 14, 46–60 (2010).
- [35] M.S. Fadali and G. Bebis, “Control system design for LTI systems using interval analysis”, ISCA Conf. on Computers and Applications 1, CD-ROM (1998).
- [36] M. Dyvak, P. Stakhiv, and A. Pukas, “Algorithms of parallel calculations in task of tolerance ellipsoidal estimation of interval model parameters”, Bull. Pol. Ac.: Tech. 60 (1), 159–164 (2012).
- [37] J. Xing, C. Chen and P. Wu, “Calculation of interval damping ratio under uncertain load in power system”, Bull. Pol. Ac.: Tech. 60 (1), 151–158 (2012).
- [38] M.D. Lorenzo del Casale, N. Femia, P. Lamberti, and V. Mainardi, “Selection of optimal closed-loop controllers for DC-DC regulators based on nominal and tolerance design”, IEEE Trans. on Industrial Electronics 51 (4), 840–849 (2004).
- [39] C.-C. Hsu, S.-C. Chang, and C.-Y. Yu, “Tolerance design of robust controllers for uncertain interval systems based on evolutionary algorithms”, IET Control Theory and Applications 1 (1), 244–252 (2007).
- [40] C.-H. Lee, Y.-H. Lee and C.-C. Teng, “A novel robust PID controllers design by fuzzy neural network”, American Control Conf. 1, 1561–1566 (2002).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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