Automated Control and Optimisation of Overhead Cranes

Automated Control and Optimisation of Overhead Cranes

Ashwani Kharola, Pravin P. Patil
DOI: 10.4018/IJMMME.2017070103
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This study considers a fuzzy based computing technique for control and optimising performance of overhead gantry crane. The objective is to minimise load swing and stabilise crane position in the least possible time. The fuzzy controllers were designed using nine gaussian and triangular shape membership functions. The results clearly confirmed the effect of shape of memberships on performance of fuzzy controllers. Performance of overhead crane was measured in terms of settling time and overshoot. The study also demonstrates the influence of varying mass of the load, mass of crane and length of crane bar on stability of the crane. A mathematical model of the crane system has been derived to develop a simulink model of proposed system and performing simulations.
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Overhead cranes are highly nonlinear systems extensively used in shipyards for transporting raw materials and finished goods due to their low cost and easy maintenance (Solihin and Wahyudi, 2009; Sagirli et al., 2011). The undesirable load swing due to external factors may cause unrestrained oscillations and vibrations resulting in stability and safety hazards (Kaur et al., 2014; Belunce et al., 2014). Therefore, the key objective is to minimise load swing and reduce travel time of load (Mahfouf et al., 2000; Chen et al., 2012). These systems are source of study and research for researchers in past few years (Santhi and Beebi, 2014). Bara et al. (2009) examined an intelligent fuzzy control for designing a microcontroller of a Mechatronic system. Real-time experiments were performed on a three dimensional crane to validate the proposed technique. A fuzzy tuned Proportional-integral-derivative (PID) controller for swing control of gantry crane was proposed by Solihin et al. (2009). The parameters of PID were tuned using gain tuners for achieving robust performance. The study also discussed dynamics analysis of the system and verified it through experimental results.

An anti-swing fuzzy control of two-dimensional overhead crane with hoisting was proposed by Trabia et al. (2006). The study applied inverse dynamics technique for determining range of variables for the controllers. The simulation results confirmed that the proposed controller successfully drives the overhead cranes under diverse operating conditions. A Sliding mode anti-swing controller for control of overhead gantry cranes was developed by Lee et al. (2006). The results showed that proposed controller resulted in increased load speed with damped load swing. Mousa (2000) designed a hybrid controller based on combination of fuzzy and time-delayed feedback technique for control of rotary cranes. Computer simulations were performed for verifying the performance of proposed controller. According to Pal and Mudi (2013) an adaptive fuzzy PID controller can be successfully applied for real-time control of an overhead crane system. The output gains of the proposed controller were tuned based on current operating conditions of the process. The study further compared fuzzy control with other conventional controllers.

Simanjalam (2012) performed modeling and control of overhead gantry crane using Euler-Lagrange formulation. The study further considered a fuzzy tuned PID controller for control of the proposed system. A twin fuzzy controller for swing and position control of overhead crane was developed by Chang et al. (2005). Experiments were conducted which demonstrated the effectiveness of proposed control scheme. An adaptive sliding mode fuzzy control of a two dimensional overhead crane was proposed by Liu et al. (2004). A model of the crane system was obtained by considering it as two independent systems in X and Y directions respectively. Fuzzy logic based servo control for anti-swing and position control of overhead crane was demonstrated by Lee and Cho (2001). The results confirmed the validity of proposed approach. Wahyudi and Jalani (2006) performed comparison of an intelligent fuzzy controller with conventional PID control. The results demonstrated that fuzzy controller was more robust to parameter variation compared to PID controller.

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