Parallel neural network combined with sliding mode control in overhead crane control system

Abstract

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.