Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 12, Issue 2, Pages 73-89

Anti-Swing Control of Gantry and Tower Cranes Using Fuzzy and Time-Delayed Feedback with Friction Compensation

H.M. Omar1 and A.H. Nayfeh2

1Department of Aerospace Engineering, King Fahd University of Petroleum and Minerals, P.O. Box 5079, Dhahran 31261, Saudi Arabia
2Department of Engineering Science and Mechanics, MC 0219, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Received 2 June 2003; Revised 26 May 2004

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We designed a feedback controller to automate crane operations by controlling the load position and its swing. First, a PD tracking controller is designed to follow a prescribed trajectory. Then, another controller is added to the control loop to damp the load swing. The anti-swing controller is designed based on two techniques: a time-delayed feedback of the load swing angle and an anti-swing fuzzy logic controller (FLC). The rules of the FLC are generated by mapping the performance of the time-delayed feedback controller. The same mapping method used for generating the rules can be applied to mimic the performance of an expert operator. The control algorithms were designed for gantry cranes and then extended to tower cranes by considering the coupling between the translational and rotational motions. Experimental results show that the controller is effective in reducing load oscillations and transferring the load in a reasonable time. To experimentally validate the theory, we had to compensate for friction. To this end, we estimated the friction and then applied a control action to cancel it. The friction force was estimated by assuming a mathematical model and then estimating the model coefficients using an off-line identification technique, the method of least squares (LS).