Adaptive observer of state variables of a nonlinear time varying system with unknown constant parameters

TL;DR

This paper develops an adaptive observer for nonlinear time-varying systems with unknown parameters, using GPEBO and least squares estimation to accurately estimate states despite nonlinearities and parameter uncertainties.

Contribution

It extends existing GPEBO-based methods to handle systems with unknown nonlinear components and unknown parameters in the control matrix.

Findings

Successful reduction to linear regression model

Effective estimation of unknown parameters

Enhanced observer design for nonlinear systems

Abstract

The paper proposes an adaptive observer of the state vector of a nonlinear time varying system based on measurements of the output variable. The problem is solved under the assumption that the control matrix (vector) and the nonlinear component of the equation of state of the system contain unknown constant parameters. When developing an adaptive observer, the GPEBO (generalized parameter estimation based observer) method was used, also known as a generalized observer based on parameter estimation, which was proposed in [1]. During the synthesis of the observer, a preliminary parametrization of the original nonlinear system is carried out. Then the resulting system is reduced to a linear regression model. At the next stage, unknown constant regression parameters are estimated using the least squares method with the forgetting factor [2, 3]. The article suggests the development of the result proposed by the authors in [4]. In [4], a linear non-stationary system containing unknown parameters in a control matrix (vector) was considered. This result is an extension of the result obtained in [4] for the case when the equation of state of the system contains a partially unknown nonlinearity.