Neural-network-based boundary control for a gantry crane system with unknown friction and output constraint.

This paper presents a neural-network-based boundary control method for a gantry system with unknown friction and output constraint. Firstly, to tackle the unknown friction, a radial basis function neural network (RBFNN) is adopted to approximate it. Secondly, we employ a barrier Lyapunov function to handle the output constraint problem. Then, a neural-network-based boundary controller is proposed to deal with the aforementioned problems. Subsequently, based on the Lyapunov stability approach, the uniformly ultimately bounded stability of the state of the closed-loop system is guaranteed. Finally, the effectiveness of the developed control method is illustrated through both numerical simulations and physical experiments. [ABSTRACT FROM AUTHOR]