Parameter Estimation-Based States Reconstruction of Uncertain Linear Systems with Overparameterization and Unknown Additive Perturbations

TL;DR

This paper introduces a new adaptive observer for uncertain linear systems with overparameterization and unknown perturbations, capable of accurately reconstructing physical states with guaranteed exponential convergence under finite excitation.

Contribution

It presents a novel adaptive observer that reconstructs physical states directly, handles fully uncertain exosystem perturbations, and guarantees exponential convergence.

Findings

The observer achieves exponential convergence of the reconstruction error.

The method is applicable to systems with fully uncertain exosystem parameters.

Simulation results validate the theoretical stability and convergence analysis.

Abstract

The problem of state reconstruction is considered for uncertain linear time-invariant systems with overparameterization, arbitrary state-space matrices and unknown additive perturbation described by an exosystem. A novel adaptive observer is proposed to solve it, which, unlike known solutions, simultaneously: (i) reconstructs the physical state of the original system rather than the virtual state of its observer canonical form, (ii) ensures exponential convergence of the reconstruction error to zero when the condition of finite excitation is satisfied, (iii) is applicable to systems, in which mentioned perturbation is generated by an exosystem with fully uncertain constant parameters. The proposed solution uses a recently published parametrization of uncertain linear systems with unknown additive perturbations, the dynamic regressor extension and mixing procedure, as well as a method of physical states reconstruction developed by the authors. Detailed analysis for stability and convergence has been provided along with simulation results to validate the theoretical analysis.