Universal approximation using differentiators and application to feedback control
X Wang - arXiv preprint arXiv:1102.2794, 2011 - arxiv.org
X Wang
arXiv preprint arXiv:1102.2794, 2011•arxiv.orgIn 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 …
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 …
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.
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