Intelligent sliding-mode motion control using fuzzy wavelet networks for automatic 3D overhead cranes

This paper develops novel methodologies for modeling and designing an intelligent sliding-mode motion control of an automated 3D overhead crane. Lagrangian mechanics is used to establish a mathematical model of the crane which involved uncertain parameters. Intelligent sliding mode control using fuzzy wavelet neural networks and backstepping are used to propose a motion controller so as to maintain the nutation angle less than 4 degrees and achieve position control simultaneously. The robust performance and merit of the proposed controller are exemplified by conducting several simulations on the 3D overhead crane with actual crane parameters under three different loading conditions.