A pure neural network controller for double-pendulum crane anti-sway control: Based on Lyapunov stability theory

Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti-sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti-sway controlling.