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A trajectory planning based controller to regulate an uncertain 3D overhead crane system

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We introduce a control strategy to solve the regulation control problem, from the perspective of trajectory planning, for an uncertain 3D overhead crane. The proposed solution was developed based on an adaptive control approach that takes advantage of the passivity properties found in this kind of systems. We use a trajectory planning approach to preserve the accelerations and velocities inside of realistic ranges, to maintaining the payload movements as close as possible to the origin. To this end, we carefully chose a suitable S-curve based on the Bezier spline, which allows us to efficiently handle the load translation problem, considerably reducing the load oscillations. To perform the convergence analysis, we applied the traditional Lyapunov theory, together with Barbalat’s lemma. We assess the effectiveness of our control strategy with convincing numerical simulations.
Rocznik
Strony
693--702
Opis fizyczny
Bibliogr. 49 poz., rys., wykr.
Twórcy
  • Research Center for Computation, National Polytechnic Institute, Av. Juan de Dios Bátiz s/n, UPALM, Col. San Pedro Zacatenco, AP 75476, 07738 Ciudad de México, México, carlosaguilari@cic.ipn.mx
  • Higher School of Computing, National Polytechnic Institute, Av. Juan de Dios Bátiz esq. Av. Miguel Othón de Mendizábal, Col. Lindavista, 07738 Ciudad de México, México, sasuarez@prodigy.net.mx
Bibliografia
  • [1] Adeli, M., Zarabadipour, H., Zarabadi, S.H. and Shoorehdeli, M.A. (2011). Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection, IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, pp. 306–311.
  • [2] Chen, H., Fang, Y. and Sun, N. (2016). A swing constraint guaranteed MPC algorithm for underactuated overhead cranes, IEEE/ASME Transactions on Mechatronics 21(5): 2543–2555.
  • [3] Cho, H.C. and Lee, K.S. (2008). Adaptive control and stability analysis of nonlinear crane systems with perturbation, Journal of Mechanical Science and Technology 22(6): 1091.
  • [4] Chwa, D. (2017). Sliding-mode-control-based robust finite-time antisway tracking control of 3-D overhead cranes, IEEE Transactions on Industrial Electronics 64(8): 6775–6784.
  • [5] Davila, J., Fridman, L. and Poznyak, A. (2006). Observation and identification of mechanical systems via second order sliding modes, International Journal of Control 79(10): 1251–1262.
  • [6] Fang, Y., Ma, B., Wang, P. and Zhang, X. (2012). A motion planning-based adaptive control method for an underactuated crane system, IEEE Transactions on Control Systems Technology 20(1): 241–248.
  • [7] Ferreira, A., Bejarano, F.J. and Fridman, L.M. (2010). Robust control with exact uncertainties compensation: With or without chattering?, IEEE Transactions on Control Systems Technology 19(5): 969–975.
  • [8] Fujioka, D., Shah, M. and Singhose, W. (2015). Robustness analysis of input-shaped model reference control on a double-pendulum crane, American Control Conference (ACC), Chicago, IL, USA, pp. 2561–2566.
  • [9] Fujioka, D. and Singhose, W. (2015a). Input-shaped model reference control of a nonlinear time-varying double-pendulum crane, 10th Asian Control Conference (ASCC), Kota Kinabalu, Malaysia, pp. 1–6.
  • [10] Fujioka, D. and Singhose, W. (2015b). Performance comparison of input-shaped model reference control on an uncertain flexible system, IFAC-PapersOnLine 48(12): 129–134.
  • [11] Gómez-Estern, F., Van der Schaft, A. and Acosta, J. (2004). Passivation of underactuated systems with physical damping, 6th IFAC Symposium on Nonlinear Control Systems, Stuttgart, Germany, pp. 1–3.
  • [12] Hajdu, S. and Gáspár, P. (2016). Reducing the mast vibration of single-mast stacker cranes by gain-scheduled control, International Journal of Applied Mathematics and Computer Science 26(4): 791–802, DOI: 10.1515/amcs-2016-0056.
  • [13] Hamid, M., Jamil, M., Gilani, S.O., Ikramullah, S., Khan, M.N., Malik, M.H. and Ahmad, I. (2016). Jib system control of industrial robotic three degree of freedom crane using a hybrid controller, Indian Journal of Science and Technology 9(21).
  • [14] Huang, Y., Xue, W., Zhiqiang, G., Sira-Ramirez, H., Wu, D. and Sun, M. (2014). Active disturbance rejection control: Methodology, practice and analysis, 33rd Chinese Control Conference, Nanjing, China, pp. 1–5.
  • [15] Jolevski, D. and Bego, O. (2015). Model predictive control of gantry/bridge crane with anti-sway algorithm, Journal of Mechanical Science and Technology 29(2): 827–834.
  • [16] Kairuz, R.I.V., Aguilar, L.T., de Loza, A.F. and Garcia, J.E.A. (2018). Robust positioning control law for a 3d underactuated crane system, IFAC-PapersOnLine 51(13): 450–455.
  • [17] Käpernick, B. and Graichen, K. (2013). Model predictive control of an overhead crane using constraint substitution, 2013 American Control Conference, Washington, DC, USA, pp. 3973–3978.
  • [18] Khalil, H.K. (2015). Nonlinear Control, Pearson, New York, NY.
  • [19] Khatamianfar, A. and Savkin, A.V. (2014). A new tracking control approach for 3D overhead crane systems using model predictive control, European Control Conference (ECC), Strasbourg, France, pp. 796–801.
  • [20] Kim, D., Park, Y., Park, Y.-s., Kwon, S. and Kim, E. (2011). Dual stage trolley control system for anti-swing control of mobile harbor crane, 11th International Conference on Control, Automation and Systems, Gyeonggi-do, Korea, pp. 420–423.
  • [21] Lee, H.-H. (2005). Motion planning for three-dimensional overhead cranes with high-speed load hoisting, International Journal of Control 78(12): 875–886.
  • [22] Lee, S.-G., Nho, L.C. and Kim, D.H. (2013). Model reference adaptive sliding mode control for three dimensional overhead cranes, International Journal of Precision Engineering and Manufacturing 14(8): 1329–1338.
  • [23] Liu, C., Zhao, H. and Cui, Y. (2014). Research on application of fuzzy adaptive PID controller in bridge crane control system, 5th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing ,China, pp. 971–974.
  • [24] Nguyen, Q.C., Ngo, H.-Q.T. and Kim, W.-H. (2015). Nonlinear adaptive control of a 3d overhead crane, 15th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea, pp. 41–47.
  • [25] Ortega, R., Perez, J.A.L., Nicklasson, P.J. and Sira-Ramirez, H.J. (2013). Passivity-Based Control of Euler–Lagrange Systems: Mechanical, Electrical and Electromechanical Applications, Springer, Berlin.
  • [26] Qian, D. and Yi, J. (2016). Hierarchical Sliding Mode Control for Under-Actuated Cranes, Springer, Heidelberg.
  • [27] Ramli, L., Mohamed, Z., Abdullahi, A.M., Jaafar, H. and Lazim, I.M. (2017). Control strategies for crane systems: A comprehensive review, Mechanical Systems and Signal Processing 95: 1–23.
  • [28] Saeidi, H., Naraghi,M. and Raie, A.A. (2013). A neural network self tuner based on input shapers behavior for anti sway system of gantry cranes, Journal of Vibration and Control 19(13): 1936–1949.
  • [29] Sano, S., Ouyang, H., Yamashita, H. and Uchiyama, N. (2011). LMI approach to robust control of rotary cranes under load sway frequency variance, Journal of System Design and Dynamics 5(7): 1402–1417.
  • [30] Sira-Ramirez, H. and Agrawal, S.K. (2004). Differentially Flat Systems, CRC Press, Boca Raton, FL.
  • [31] Smoczek, J. (2013). Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control, International Journal of Applied Mathematics and Computer Science 23(4): 749–759, DOI: 10.2478/amcs-2013-0056.
  • [32] Smoczek, J. (2015). Experimental verification of a GPC-LPV method with RLS and P1-TS fuzzy-based estimation for limiting the transient and residual vibration of a crane system, Mechanical Systems and Signal Processing 62: 324–340.
  • [33] Smoczek, J. and Szpytko, J. (2017). Particle swarm optimization-based multivariable generalized predictive control for an overhead crane, IEEE/ASME Transactions on Mechatronics 22(1): 258–268.
  • [34] Solis, C.U., Clempner, J.B. and Poznyak, A.S. (2016). Designing a terminal optimal control with an integral sliding mode component using a saddle point method approach: A Cartesian 3D-crane application, Nonlinear Dynamics 86(2): 911–926.
  • [35] Spathopoulos, M. and Fragopoulos, D. (2001). Control design of a crane for offshore lifting operations shore crane, in A. Isidori et al. (Eds.), Nonlinear Control in the Year 2000, Springer, London, pp. 469–486.
  • [36] Spathopoulos, M. and Fragopoulos, D. (2004). Pendulation control of an offshore crane, International Journal of Control 77(7): 654–670.
  • [37] Suh, J.-H., Lee, J.-W., Lee, Y.-J. and Lee, K.-S. (2005). Anti-sway position control of an automated transfer crane based on neural network predictive PID controller, Journal of Mechanical Science and Technology 19(2): 505–519.
  • [38] Sun, N., Fang, Y. and Chen, H. (2014). Adaptive control of underactuated crane systems subject to bridge length limitation and parametric uncertainties, 33rd Chinese Control Conference (CCC), Nanjing, China, pp. 3568–3573.
  • [39] Sun, N., Fang, Y. and Chen, H. (2015a). Adaptive antiswing control for cranes in the presence of rail length constraints and uncertainties, Nonlinear Dynamics 81(1–2): 41–51.
  • [40] Sun, N., Fang, Y., Chen, H. and He, B. (2015b). Adaptive nonlinear crane control with load hoisting/lowering and unknown parameters: Design and experiments, IEEE/ASME Transactions on Mechatronics 20(5): 2107–2119.
  • [41] Sun, N., Fang, Y., Chen, H., Lu, B. and Fu, Y. (2016). Slew/translation positioning and swing suppression for 4-DOF tower cranes with parametric uncertainties: Design and hardware experimentation, IEEE Transactions on Industrial Electronics 63(10): 6407–6418.
  • [42] Tar, J.K., Rudas, I.J., Bitó, J.F., Machado, J.A.T. and Kozłowski, K.R. (2010). Adaptive tackling of the swinging problem for a 2 DOF crane–payload system, in I.J. Rudas et al. (Eds), Computational Intelligence in Engineering, Springer, Berlin/Heidelberg, pp. 103–114.
  • [43] Vazquez, C., Fridman, L. and Collado, J. (2012). Second order sliding mode control of a 3-dimensional overhead-crane, 51st Annual Conference on Decision and Control (CDC), Maui, HI, USA, pp. 6472–6476.
  • [44] Vázquez, C., Fridman, L., Collado, J. and Castillo, I. (2015). Second-order sliding mode control of a perturbed-crane, Journal of Dynamic Systems, Measurement, and Control 137(8): 081010.
  • [45] Vukov, M., Van Loock, W., Houska, B., Ferreau, H.J., Swevers, J. and Diehl, M. (2012). Experimental validation of nonlinear MPC on an overhead crane using automatic code generation, American Control Conference (ACC), Montréal, Canada, pp. 6264–6269.
  • [46] Wu, Z., Xia, X. and Zhu, B. (2015). Model predictive control for improving operational efficiency of overhead cranes, Nonlinear Dynamics 79(4): 2639–2657.
  • [47] Yang, J.H. and Shen, S.H. (2011). Novel approach for adaptive tracking control of a 3-d overhead crane system, Journal of Intelligent & Robotic Systems 62(1): 59–80.
  • [48] Yu, W., Li, X. and Panuncio, F. (2014). Stable neural PID anti-swing control for an overhead crane, Intelligent Automation & Soft Computing 20(2): 145–158.
  • [49] Zheng, Q. and Gao, Z. (2010). On practical applications of active disturbance rejection control, 29th Chinese Control Conference, Beijing, China, pp. 6095–6100.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-03a7b935-8022-41d3-8a51-6c6455d2e70f
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