Adaptive Finite-time Dynamic Surface Neural Network Control of an Uncertain Robot with Output Constraint and Input Saturation

Abstract

In this study, a finite-time dynamic surface neural network control is developed for an uncertain n-link robot subject to input saturation and output constraints. First, a barrier Lyapunov function and a hyperbolic tangent function are applied to solve the system constraints using a dynamic surface control. Subsequently, a radial basis function neural network is utilized to handle system uncertainties. Then, a finite-time filter is employed in the design to achieve the fast convergence and a Nussbaum function is employed to optimize the design process. Finally, the simulation results show that the dynamic tracking error is proved to converging to zero, and the proposed control method is effective and never violates the constraints.