Finite-time adaptive neural control of nonlinear systems with unknown output hysteresis

Abstract

This paper aims to address the problem of the adaptive finite-time neural control for a class of nonlinear systems with the dynamic disturbance and output hysteresis. The Bouc–Wen model is first introduced to capture the output hysteresis phenomenon. The variable-transformed method is employed to resolve the problem that x1 cannot be available for measurement because of the output hysteresis. Furthermore, for the sake of conquering the output hysteresis constraint, the adaptive backstepping control and ln-type barrier Lyapunov function (BLF) are combined in a unified framework, which can guarantee the prescribed constraint of the tracking error. In addition, the Nussbaum function is used to deal with the unknown control gain problem (UCGP). Basing on the new finite-time stability criterion, an adaptive finite time controller is constructed, which can ensure that the closed-loop system is segi-global practical finite-time stability (SGPFS). The system states remain in the defined compact sets and the output constraint is not violated. Finally, the simulation is implemented to evaluate the effectiveness of the proposed scheme.