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Shock and Vibration
Volume 10, Issue 2, Pages 81-95

Control of Rotary Cranes Using Fuzzy Logic

Amjed A. Al-mousa,1 Ali H. Nayfeh,2 and Pushkin Kachroo3

1Software Engineer, Intel Corporation, Santa Clara, CA 95053, USA
2Department of Engineering Science and Mechanics, MC 0219, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
3Department of Bradley Electrical and Computer Engineering, MC 0111, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Received 7 December 2001; Revised 6 August 2002

Copyright © 2003 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.


Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them has become difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control attractive.

In this work a fuzzy logic controller is introduced with the idea of “split-horizon”; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent sub-controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FIE). Computer simulations are used to verify the performance of the controller. Three simulation cases are presented. In the first case, the crane is operated in the gantry (radial) mode in which the trolley moves along the jib while the jib is fixed. In the second case (rotary mode), the trolley moves along the jib and the jib rotates. In the third case, the trolley and jib are fixed while the load is given an initial disturbance. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing the maneuvers in relatively reasonable times.