Adaptive State Observers of Linear Time-Varying Descriptor Systems: A Parameter Estimation-Based Approach

In this paper, we apply the recently developed generalized parameter estimation-based observer design technique for state-affine systems to the practically important case of linear time-varying descriptor systems with uncertain parameters. We proceed from the general description of the system given by the Standard Canonical Form and try to develop a comprehensive theory for the design of adaptive observers for these systems. A consequence of our intention to keep the analysis as general as possible is that the final results are a little bit cryptic. Certainly, proceeding from this general framework, we can address particular cases and achieve some practically workable designs. We have identified several critical aspects of the problem. For instance, the highly relevant role played by the strangeness index and underscore the differences between the time-variant and the time-invariant cases, which are quite significant. We give simulation results of benchmark examples that illustrate the performance of the proposed adaptive observer.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Automatic Control
Published
2025-06-24
DOI
https://doi.org/10.1109/tac.2025.3582735
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
1.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive State Observers of Linear Time-Varying Descriptor Systems: A Parameter Estimation-Based Approach

Nikolay Nikolaev, Roméo Ortega, Fernando Castaños, Alexey Bobtsov
1 citations
IEEE Transactions on Automatic Control
Fault Detection and Control Systems
1.21
article

Adaptive State Observers of Linear Time-Varying Descriptor Systems: A Parameter Estimation-Based Approach

Nikolay Nikolaev, Roméo Ortega, Fernando Castaños, Alexey Bobtsov
article en
1 citations

Abstract

In this paper, we apply the recently developed generalized parameter estimation-based observer design technique for state-affine systems to the practically important case of linear time-varying descriptor systems with uncertain parameters. We proceed from the general description of the system given by the Standard Canonical Form and try to develop a comprehensive theory for the design of adaptive observers for these systems. A consequence of our intention to keep the analysis as general as possible is that the final results are a little bit cryptic. Certainly, proceeding from this general framework, we can address particular cases and achieve some practically workable designs. We have identified several critical aspects of the problem. For instance, the highly relevant role played by the strangeness index and underscore the differences between the time-variant and the time-invariant cases, which are quite significant. We give simulation results of benchmark examples that illustrate the performance of the proposed adaptive observer.

IEEE Transactions on Automatic ControlVol. 71(1)
ITMO University (RU), Technion – Israel Institute of Technology (IL), Instituto Tecnológico Autónomo de México (MX), China XD Group (China) (CN), Hangzhou Dianzi University (CN), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (MX)
Openalex Percentile: Top 20%
Fault Detection and Control Systems
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Adaptive State Observers of Linear Time-Varying Descriptor Systems: A Parameter Estimation-Based Approach — Nikolay Nikolaev, Roméo Ortega, et al. · IEEE Transactions on Automatic Control (2025) | TGRS Research Map | TGRS