On the existence of proportional-integral observer for the state estimation of linear time-invariant systems

Abstract In this paper, the explicit necessary and sufficient conditions are established for the existence of proportional-integral observer for the state estimation of linear time-invariant continuous-time systems. In particular, it is proven that for a given linear time-invariant continuous-time system of order n , having m inputs and p linearly independent outputs, a proportional-integral observer of order n can be constructed if and only if the given system is detectable. Furthermore a simple procedure is given for the construction of proportional-integral observer. Our approach is based on properties of real and polynomial matrices.

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Publication Details

Journal
Control and Cybernetics
Published
2025-03-01
DOI
https://doi.org/10.2478/candc-2025-0002
Primary Topic
Stability and Control of Uncertain Systems
Type
article
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On the existence of proportional-integral observer for the state estimation of linear time-invariant systems

Konstadinos H. Kiritsis
Control and Cybernetics
Stability and Control of Uncertain Systems
article

On the existence of proportional-integral observer for the state estimation of linear time-invariant systems

Konstadinos H. Kiritsis
article en

Abstract

Abstract In this paper, the explicit necessary and sufficient conditions are established for the existence of proportional-integral observer for the state estimation of linear time-invariant continuous-time systems. In particular, it is proven that for a given linear time-invariant continuous-time system of order n , having m inputs and p linearly independent outputs, a proportional-integral observer of order n can be constructed if and only if the given system is detectable. Furthermore a simple procedure is given for the construction of proportional-integral observer. Our approach is based on properties of real and polynomial matrices.

Control and CyberneticsVol. 54(1)
Hellenic Air Force (GR)
Openalex Percentile: Top 53%
Stability and Control of Uncertain Systems
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