Anti-swing control of a new container crane with fuzzy uncertainties compensation

A class of new container cranes with eight-link lifting mechanics which can reduce sway efficiently as a result of improved lifting structure have been put to use recently, and both modeling and swing control of such a crane system attracts a lot attention in the field of control technology development. In this paper, dynamics model of the new container crane is investigated, an anti-swing control scheme with fuzzy uncertainty compensation is proposed to ensure the positioning control as well as overall closed-loop system stability. None of the system parameters is required for the controller design in a priori. In the proposed control laws, the position error can be driven to a bounded area while the swing angle can also be rapidly damped so as to achieve minimal sway of the crane system. Stability analysis of the controller is also given. Finally, simulation results show the performance successfully.

[1]  Mohd Ashraf Ahmad,et al.  Hybrid Fuzzy Logic Control with Input Shaping for Input Tracking and Sway Suppression of a Gantry Crane System , 2009 .

[2]  Joaquin Collado,et al.  Optimal delayed control for an overhead crane , 2009, 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[3]  Suk-Kyo Hong,et al.  Adaptive fuzzy nonlinear anti-sway trajectory tracking control of uncertain overhead cranes with high-speed load hoisting motion , 2007, 2007 International Conference on Control, Automation and Systems.

[4]  Cheng-Yuan Chang,et al.  Intelligent fuzzy accelerated method for the nonlinear 3-D crane control , 2009, Expert Syst. Appl..

[5]  Cheng-Yuan Chang,et al.  Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance , 2007, IEEE Transactions on Industrial Informatics.

[6]  Euntai Kim,et al.  A fuzzy disturbance observer and its application to control , 2002, IEEE Trans. Fuzzy Syst..

[7]  Aydin Yesildirek Intelligent control of overhead gantry cranes , 2009, 2009 6th International Symposium on Mechatronics and its Applications.

[8]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[9]  W. Singhose,et al.  Study of operator behavior, learning, and performance using an input-shaped bridge crane , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[10]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[11]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[12]  William Singhose,et al.  Command shaping for flexible systems: A review of the first 50 years , 2009 .

[13]  Andreas Johansson,et al.  A Sufficient Condition for Invalidation of Linear State-Space Systems With Uncertain Time-Varying Parameters , 2009, IEEE Transactions on Automatic Control.

[14]  J.H. Yang,et al.  Adaptive control for 3-D overhead crane systems , 2006, 2006 American Control Conference.

[15]  William Singhose,et al.  A controller enabling precise positioning and sway reduction in bridge and gantry cranes , 2007 .

[16]  J. Ngamwiwit,et al.  I-PD and PD controllers designed by CRA for overhead crane system , 2007, 2007 International Conference on Control, Automation and Systems.

[17]  William T. Baumann,et al.  Nonlinear analysis of time-delay position feedback control of container cranes , 2008 .

[18]  Dongkyoung Chwa Nonlinear Tracking Control of 3-D Overhead Cranes Against the Initial Swing Angle and the Variation of Payload Weight , 2009, IEEE Transactions on Control Systems Technology.

[19]  Ton J. J. van den Boom,et al.  Real-time time-optimal control for a nonlinear container crane using a neural network , 2005, ICINCO.

[20]  S. Sul,et al.  Anti-Sway Control of Container Cranes: Inclinometer, Observer, and State Feedback , 2004 .

[21]  Wang Xi-huai Simulation of the anti-sway system of container crane based on MATLAB , 2008 .