Anti-Sway and Positioning Adaptive Control of a Double-Pendulum Effect Crane System With Neural Network Compensation - Qiang, Hai-yan; Sun, You-gang; Lyu, Jin-chao; Dong, Da-shan (Frontiers in Robotics and AI, 2021)
Cranes are widely used in the field of construction, logistics, and manufacturing industry. And cranes that use wire ropes as the main lifting mechanism are deeply troubled by the swaying of heavy objects, which seriously restricts the working efficiency of the crane and evenly cause accidents. Compared with the single pendulum crane, the double-pendulum effect crane model has stronger nonlinearity, and its controller design are challenging. In this paper, the bridge cranes with double-pendulum effect are considered and its nonlinear dynamical models are also established. Then, a controller based on radial basis function (RBF) neural network compensation adaptive method is designed and the related stability analysis is also presented. Finally, the hardware-in-the-loop experimental results show that the neural network compensation control can effectively improve the control performance of the controller in practice.