Real-Time Visual Tracking and Measurement to Control Fast Dynamics of Overhead Cranes

Abstract

Applying visual tracking technology to dynamic systems is important to many industrial applications. However, image sensing is difficult to implement in a fast dynamic plant without high-speed computational resources. This work presents a simple yet effective method of image processing to capture the dynamic movement of an overhead crane, enabling it to be controlled in real time. Visual tracking based on color histograms involves comparison of the color in a model image with the color in a sequence of images to track a dynamic object. The best match is identified using Bhattacharyya similarity measurement. Upon tracking, the sensing data are sent to an adaptive fuzzy sliding mode controller (AFSMC). The advantages of this approach include robustness and the model-free properties of the sliding mode and the fuzzy logic controllers; adaptable slopes of the sliding surface of AFSMC are also presented to improve control performance.