Nonfragile high-gain observers for nonlinear systems with output uncertainty

Abstract

In this paper, we present the definitions of nonfragile high-gain observers and design method for lower-triangular nonlinear systems with output uncertainty. Radial basis function neural networks (RBFNNs) are used to approximate the output uncertainty. By inserting an output filter and an input-output filter, a new augmented adaptive observable canonical form is derived. Then, a corresponding observer with gain perturbations is designed to estimate the states and the coefficients of the RBFNNs, and a disturbance observer is designed to estimate the approximation error. The maximum allowable gain perturbation is also given. Then, the obtained results are extended to nonlinear systems in adaptive observer form with output uncertainty. Finally, some numerical simulations are offered to corroborate the theoretical results.