Autonomous Overhead Crane System Using a Fuzzy Logic Controller

Abstract

This paper pertains to advanced automation of the load transfer process using overhead cranes. Overhead cranes are widely used in various areas of industry, including manufacturing, construction, shipping, etc. Load transfer operations using overhead cranes have to be performed fast and safely. As such, these operations are handled by expert operators1 however, the demand for an automatic consistent and reliable crane operation is on the rise. The crane-load system is highly nonlinear and time-varying, hence, solutions considering model-base approaches may lead to a complicated controller structure. Such a controller may require accurate estimation of the crane system parameters. In this paper we present a new fuzzy logic controller for overhead crane operation. The fuzzy controller is designed based on knowledge of an expert crane operator, and does not require any parameter estimation. It mimics the operator behavior by using the same crane-load system states that are realized by the operator. These states are the trolley position error and the load sway angle. The fuzzy controller action, on the other hand, is the desired trolley speed. The proposed controller is implemented and tested on a small-scale overhead crane. Experimental results show robust operation of the fuzzy controller as compared with that of a conventional controller.

Fuzzy sliding mode control of an offshore container crane

“…From the crane control aspect, various control algorithms have been proposed to deal with sway suppression. These methods include open-loop control, such as input shaping control for gantry cranes, bridges (Ngo et al, 2012;Hong et al, 2003;Huey et al, 2008;Robertson and Singhose, 2009;Singhose et al, 2000;Sorensen et al, 2007;Sorensen and Singhose, 2008;Sung and Singhose, 2009a; Sung and Singhose, 2009b), and flexible systems in general (Hong et al, 2003;Huey et al, 2008;Robertson and Singhose, 2009;Singhose et al, 2000;Sorensen et al, 2007;Sorensen and Singhose, 2008;Sung and Singhose, 2009a;Sung and Singhose, 2009b) as well as closed-loop control, such as optimal control (Al-Garni et al, 1995;Hong et al, 2000), state feedback control (Kim et al, 2004;Kłosiński, 2005;Messineo et al, 2008;Park et al, 2007;Sawodny et al, 2002), fuzzy control (Ahmad, 2009;Benhidjeb and Gissinger, 1995;Chang and Chiang, 2008;Chen et al, 2009;Cho and Lee, 2002;Omar et al, 2004), adaptive control (Cheng-Yuan, 2007;Liu et al, 2005;Messineo and Serrani, 2009;Mizumoto et al, 2007;Ngo and Hong, 2012a;Tuan et al, 2013;Yang and Yang, 2007), and robust control…”

Application of Machine Vision to Collision Avoidance Control of the Overhead Crane

“…That study used sliding mode controller and visual feedback to eliminate the swing caused by the sliding of overhead crane, thus reducing the risks of impacting persons. Some researchers used fuzzy control to improve the swing of hanging object caused by the sliding of overhead crane [3], and applied nonlinear coupling control law or fuzzy control to the overhead crane [4][5]. The above studies used the reduced model of overhead crane for test, but did not apply the model in actual operation.…”

Use of balancing rope for damping of cargo sways moved by a loading crane

“…Одним из ее важных аспектов является подавление неуправляемых колебаний груза большой амплитуды, которые возникают при перемещении краном груза на нежестком канатном подвесе [4][5][6]. Решению данной проблемы посвящено множество работ российских авторов [1,7,8] и зарубежных исследователей [9][10][11]. Данная проблема актуальная для всех видов грузоподъемных кранов с нежестким канатным подвесом: мостовых [12-14], козловых [15], поворотных с телескопической стрелой [16,17], контейнерных [18], судовых [19], башенных [20,21] и крановтрубоукладчиков [22,23].…”