This proposed work designs controller using vision feedback for an overhead crane. This approach searches the image features and computes the useful vision based information in terms of several tracking areas block in each frame. According to the lightest or darkest point in the tracking area of a dynamic object; this work determines the trolley position and load swing for controller design. Experimental results verify effectiveness of the proposed work.
Using visual tracking technology by CCD sensor instead of high speed computational resources to measure the fast dynamic systems is not easy. This paper proposes a simple and effective method to do the image processing, catching this dynamic movement in real-time and controlling the overhead crane. Visual tracking based on color histograms will compare the color in a model image with the color in image sequences to track the dynamic object. Once it tracked, the sensing data will be sent to the adaptive fuzzy sliding mode controller (AFSMC) to control the overhead cranes. The merits of it include the robustness and model free properties of the sliding mode and fuzzy logic controllers; adaptable slopes of the sliding surface are also presented to enhance the control results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.