Neural network-based boundary control of a gantry crane system subject to input deadzone and external disturbance

This paper is concerned with designing a neural network-based boundary controller to stabilize the gantry crane system subject to input deadzone and external disturbance. More explicitly, a boundary controller acting on the trolley is developed together with adopting a neural network to compensate for the unknown nonlinearity of the input deadzone. Subsequently, based on the Lyapunov stability theory, we show that the solution to the closed-loop system is both uniformly bounded and uniformly ultimately bounded. Finally, both numerical simulations and physical experiment results are provided to demonstrate the effectiveness of the proposed method.