Neural-network tracking control of space robot based on sliding-mode variable structure

This paper investigates the tracking problem of space robot with uncertainties, without using the estimation values of a model, and puts forward a neural-network control scheme with sliding-mode variable structure. A radial-basisfunction(RBF) neural-network controller based on Lyapunov theory is designed to compensate for the unknown nonlinearity in the system. The neural-network controller guarantees the stability of the closed-loop system. The controller that integrates the neutral network with the variable structure by saturation function not only effectively eliminates the chattering in sliding-mode input, but also maintains the robustness of the closed-loop system when the neutral-network controller fails. Simulation results show the desirable performances of the presented controller in the early phase of operation and in the strong disturbance situation.