A novel control for a nonlinear two-dimensional (2-D) overhead crane is proposed. Instead of the complex design procedures used in classic methods, the proposed scheme combines the principles of neural networks (NNs) and variable structure systems (VSS) to derive control signals needed to drive the cart smoothly, rapidly and with limited payload swing. The merits include the robustness and model-free properties of the sliding mode and neural based controllers, respectively. Simulations performed using a scaled 2-D mathematical model of the crane confirm the effectiveness of the proposed method.

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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.