Year: 2011
Type: preprint
Abstract: In this paper, we consider the problems of approximating uncertainties and feedback control for a class of nonlinear systems without full-known states, and two approximation methods are proposed: universal approximation using integral-chain differentiator or extended observer. Comparing to the approximations by fuzzy system and radial-based-function (RBF) neural networks, the presented two methods can not only approximate universally the uncertainties, but also estimate the unknown states. Moreover, the integral-chain differentiator can restrain noises thoroughly. The theoretical results are confirmed by computer simulations for feedback control.
Source: arXiv (Cornell University)
Author: Wang, Xinhua
Institution: National University of Singapore
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Award: 带内模扰动的非线性非最小相位系统输出跟踪研究
OpenAlex ID: W1598847785