A data-driven approach for modifying the rope dynamics model of the flexible hoisting system

Abstract

In the flexible hoisting system, past research focused on the physical modeling without considering complex external environmental variables such as guided rails excitation and shaft effect, leading to a significant deviation between the physical model and the actual model. However, the physical modeling is difficult to model the actual dynamics of the rope strictly under the actual working conditions. This paper takes a high-speed elevator hoisting system as an example. A modified model combining the physical model and the data-driven model is proposed to mitigate the deviation between the physical model and the actual model. In the experiments, the vibration signals of the rope were extracted from images collected by a camera. A beat-like phenomenon of the vibration signals is discovered in the vibration signals of the rope during the acceleration stage. The experiment results demonstrate that the modified model can more accurately model the dynamics of the rope under the actual working conditions and reduce the absolute error of 75.9% compared with the physical model. The proposed model also provides a reference for the modification of the complex dynamic models.