Control Optimisation of Overhead Gantry Cranes via Fuzzy Controllers

Introduction

Overhead cranes are extremely nonlinear systems extensively used in industries for carrying materials from one location to another (Solihin and Wahyudi, 2009; Sagirli et al., 2011). These machines are widely used for transportation as they provide faster transportation at a comparatively low cost with minimal safety constraints (Fedtke & Boysen, 2017). The undesirable swing due to external forces may cause unrestrained oscillations and vibrations resulting in stability and safety hazards of these machines (Kaur et al., 2014; Belunce et al., 2014). Therefore, the key objective while operating these machines is to minimise load swing in least possible travel time (Mahfouf et al., 2000; Chen et al., 2012). The overhead gantry cranes are keen source of study and research for researchers in past few decades because of major industrial applications (Santhi and Beebi, 2014). A novel adaptive hierarchical sliding mode control of three dimensional overhead crane was presented by Le et al. (2019). The proposed control has enhanced the robustness of the crane system under uncertainty conditions and was designed using radial basis neural network derived from Lyapunov function. An active disturbance rejection model for the problem of load transportation and oscillation for tower crane was developed by Coral-Enriquez et al. (2019). The authors formulated a disturbance observer for the proposed system which aided in better estimation of system properties and disturbances. Singh and Ha (2019) proposed a finite control time method based on terminal sliding mode for control of underactuated gantry crane system. The authors defined hierarchical sliding surfaces comprising of two layers for underactuated system control. A model predictive control which provided faster transportation of cargo with minimal swing was proposed by Jolevski and Bego (2015). A multi-criteria optimization technique was adopted to create the solution function for the proposed control. Experiments were performed on the laboratory model of the crane system to confirmed the validity of the proposed study. 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.