LEARNING BASED ANTI-SWAY CONTROL OF OVERHEAD CRANES

Abstract

In this report we are discussing the Learning based Anti-Sway control of the Overhead Cranes. For that we firstly discuss what is the problem of sway and how it is affecting our industries. To mitigate the effects of sway, various anti-sway control strategies have been developed. These strategies can be broadly classified into passive and active methods. Passive methods include using outriggers or counterweights to increase the stability of the crane, while active methods involve using sensors, controllers, and actuators to actively control the motion of the crane. Now, we discuss what Anti-Sway control is and its types and then we discuss the mathematical model for the crane, further our aim is to solve the Anti- Sway control problem with the help of Reinforcement Learning. Modern cranes are typically equipped with sophisticated anti-sway control systems that use a combination of passive and active methods. These systems can be programmed to adapt to different load characteristics and environmental conditions, ensuring safe and efficient operation. Antisway control systems have become an essential component of crane technology, and their continued development and improvement are critical for ensuring safety and productivity in the construction industry.