Neural sliding mode control with finite time convergence

Abstract

Combination of neural networks and sliding mode control (SMC) can reduce chattering, because the upper bound of uncertainties becomes smaller when neural networks are used to model unknwn nolinear systems. The tracking error of normal neural sliding mode control is asymptotically stable, while neural control and SMC are applied at same time. In this paper, neural control and SMC are connected serially: first a deadzone neural control assures that the tracking error is bounded, then super-twisting secondorder slidingmode is used to guarantee finite time convergence of the contoller.