Comparison of different sway control algorithms for multi carriages crane system

• Published in: Soft computing (Berlin, Germany) Vol. 29; no. 8; pp. 4001 - 4020
• Main Authors: Esim, Emir, Yıldırım, Şahin, Gördebil, Ayşegül
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2025; Springer Nature B.V
• Subjects: Adaptive systems; Application of Soft Computing; Artificial Intelligence; Carriages; Cartesian coordinates; Computational Intelligence; Control; Control algorithms; Control methods; Control systems; Controllers; Cranes; Cranes & hoists; Dynamic models; Engineering; Factories; Fuzzy logic; Genetic algorithms; Investigations; Mathematical Logic and Foundations; Mechatronics; Medical diagnosis; Neural networks; Proportional integral derivative; Robotics; United States > US; Colorado; Overhead crane system; Fuzzy logic; Genetic algorithm based PID control (GAPID); Anti-sway control; Proportional integral derivative (PID) control; Adaptive neuro-fuzzy inference system(ANFIS)
• ISSN: 1432-7643; 1433-7479; 1433-7479
• DOI: 10.1007/s00500-025-10544-9

Crane systems, are widely used for the transport of heavyweight and high-volume loads in various fields. Increasing the number of carriages in the crane system is a method frequently used in the industry so that the load is in the form of a distributed load. For applications with high sensitivity needs, anti-swing control of multi-car crane systems should also be performed. In this study, a dynamic model of an overhead crane with three cars operating in cartesian coordinates is derived. In this model, six different methods were used for oscillation reduction. Control methods are traditional proportional + integral + derivative (PID), fuzzy logic, fuzzy logic-based adaptive PID, Adaptive Neuro-Fuzzy Inference System(ANFIS), combined ANFIS-PID, and Genetic Algorithm Based PID Control (GAPID) methods. The reason for choosing the six methods here is to compare their performance in multi-car crane systems in the case of classic, modern control, and hybrid use. Model system and control systems were established and simulated in MATLAB Simulink environment. By comparing the obtained results with each other, it has been examined which techniques are more successful in controlling this system. Finally, the performance of the ANFIS–PID control system is better than other controllers. The simulation results obtained in this study are expected to contribute to experimental studies.