Adaptive neural network control of flexible robotic with unmodeled dynamics and time-varying output constraints

Abstract

In this paper, an adaptive dynamic surface control (DSC) method is proposed for the flexible robotic system with unmodeled dynamics and time-varying output constraints. The dynamic disturbances are effectively dealt with by introducing a dynamic signal. The unknown continuous functions are approximated by using radial basis function neural networks (RBFNNs). An asymmetric time-varying barrier Lyapunov function (BLF) is employed to ensure constraint satisfaction. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded. Simulation results are provided to illustrate the effectiveness of the proposed approach.