Disturbance observer–based adaptive neural finite-time control for nonstrict-feedback nonlinear systems with input delay
Fansen Wei, Ning Xu, Sai Huang, Yumeng Cao
Abstract
In this paper, the problem of adaptive finite-time tracking control is investigated for nonstrict-feedback nonlinear systems with input delay. First, a novel form of auxiliary systems is presented as a feasible approach to compensate for the influence of input delay. Second, by leveraging a disturbance observer to approximate unknown disturbances, the disturbance rejection capability of nonlinear system is improved. Then, a novel finite-time command filter is introduced to cope with the problem of explosion of complexity, and the filtering errors are compensated within a specific time period by constructing an error compensation signal. Based on the finite-time stability theory, the tracking error is verified to converge to a prescribed performance range within a finite time, and the boundedness of all signals in the closed-loop system is ensured. Finally, simulation results are presented to demonstrate the effectiveness of the developed adaptive control scheme.