{"title":"Distributed UIO for Multiple Sensor Systems With Unknown Inputs and Measurement Noise","authors":"Fanglai Zhu, Mingwei Li, Tianbiao Shi, Xufeng Ling","doi":"10.1002/acs.70089","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In the article, a distributed unknown input observer (DUIO) constructing technique for a system with unknown input (UI) and measurement noise (MN) is come up with. Specially, multiple sensor groups are equipped to measure the output of the target system, and the output information measured by each sensor group is not enough for estimating the system states individually. It is assumed that each sensor group suffers from MN. To begin with, an output transformation and a state shift transformation are made such that the transformed subsystem contains neither UI nor MN. Next, for each transformed subsystem, a distributed state observer is designed to obtain the asymptotic convergent state estimation, and also the MN reconstruction (MNR). Furthermore, an output interval estimation method is proposed, and a correlation of the UI and the state is come up with algebraically. Then, by using the relationship, an asymptotic convergent UI reconstruction (UIR) method is developed. Finally, a simulation example is given to verify the effectiveness of the proposed method.</p>\n </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"40 8","pages":"1743-1754"},"PeriodicalIF":3.8000,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Adaptive Control and Signal Processing","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acs.70089","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/4/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In the article, a distributed unknown input observer (DUIO) constructing technique for a system with unknown input (UI) and measurement noise (MN) is come up with. Specially, multiple sensor groups are equipped to measure the output of the target system, and the output information measured by each sensor group is not enough for estimating the system states individually. It is assumed that each sensor group suffers from MN. To begin with, an output transformation and a state shift transformation are made such that the transformed subsystem contains neither UI nor MN. Next, for each transformed subsystem, a distributed state observer is designed to obtain the asymptotic convergent state estimation, and also the MN reconstruction (MNR). Furthermore, an output interval estimation method is proposed, and a correlation of the UI and the state is come up with algebraically. Then, by using the relationship, an asymptotic convergent UI reconstruction (UIR) method is developed. Finally, a simulation example is given to verify the effectiveness of the proposed method.
期刊介绍:
The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material.
Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include:
Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers
Nonlinear, Robust and Intelligent Adaptive Controllers
Linear and Nonlinear Multivariable System Identification and Estimation
Identification of Linear Parameter Varying, Distributed and Hybrid Systems
Multiple Model Adaptive Control
Adaptive Signal processing Theory and Algorithms
Adaptation in Multi-Agent Systems
Condition Monitoring Systems
Fault Detection and Isolation Methods
Fault Detection and Isolation Methods
Fault-Tolerant Control (system supervision and diagnosis)
Learning Systems and Adaptive Modelling
Real Time Algorithms for Adaptive Signal Processing and Control
Adaptive Signal Processing and Control Applications
Adaptive Cloud Architectures and Networking
Adaptive Mechanisms for Internet of Things
Adaptive Sliding Mode Control.