Tension coordination control of double-rope winding hoisting system using hybrid learning control scheme

Abstract

Tension difference between ropes due to asynchronous hoisting, guide rail tilt, and friction jam restricts the application of double-rope winding hoisting systems that have high-security requirement. In this article, a hybrid adaptive iterative learning control scheme is presented for a double-rope winding hoisting system driven by permanent magnet synchronous motor systems. First, based on the discrete model of the wire rope, the mathematical model of the system is established. Subsequently, in order to reduce the tension difference of the wire ropes under impact, a hybrid control scheme based on iterative learning control and radial basis function neural network is proposed to improve the performance of the controller. A radial basis function neural network–based adaptive law is developed to compensate the uncertainties of the movable headgear sheave subsystem, and radial basis function neural network–based switching gains are applied to improve the disturbance compensation speed of the iterative learning controller. Stability of the overall closed-loop system under proposed controller is proved. Finally, the experimental results show that the proposed controller is effective and has better performance than traditional controllers.