Abstract
Abstract The paper presents the methodology of prototyping the adaptive neuro-fuzzy anti-sway overhead traveling crane control system. The aim of control system is to reduce swing of a payload shifted by crane and to obtain expected position with assumed precision taking into consideration rope length and mass of a load variables. Proposed control system was based on the Takagi-Sugeno-Kang (TSK) neuro-fuzzy controller elaborated in the process of artificial neural network learning. Training data used in the learning process were obtained from conventional discrete control systems that were worked out using pole placement method (PPM) for parametric models of the controlled object. The control system was tested on the real object and the results of experiments are presented in the paper.
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