Adaptive Observers and Parameter Estimation for a Class of Systems Nonlinear in the Parameters

TL;DR

This paper introduces a generalized adaptive observer framework for estimating states and parameters in nonlinear parameterized systems of differential equations, extending traditional methods under certain excitation conditions.

Contribution

It proposes a novel approach for asymptotic state and parameter reconstruction in nonlinear systems, generalizing standard adaptive observer designs.

Findings

Effective reconstruction under persistency of excitation.

Reduction to standard estimators in linear cases.

Applicable to a broad class of nonlinear systems.

Abstract

We consider the problem of asymptotic reconstruction of the state and parameter values in systems of ordinary differential equations. A solution to this problem is proposed for a class of systems of which the unknowns are allowed to be nonlinearly parameterized functions of state and time. Reconstruction of state and parameter values is based on the concepts of weakly attracting sets and non-uniform convergence and is subjected to persistency of excitation conditions. In absence of nonlinear parametrization the resulting observers reduce to standard estimation schemes. In this respect, the proposed method constitutes a generalization of the conventional canonical adaptive observer design.