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System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.


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Journal ArticleDOI
Zhi Ding1
TL;DR: This paper presents a new algorithm that utilizes second-order statistics for multichannel parameter estimation that is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimates.
Abstract: Blind channel identification and equalization have attracted a great deal of attention due to their potential application in mobile communications and digital TV systems. In this paper, we present a new algorithm that utilizes second-order statistics for multichannel parameter estimation. The algorithm is simple and relies on an outer-product decomposition. Its implementation requires no adjustment for single- or multiple-user systems. This new algorithm can be viewed as a generalization of a linear prediction algorithm. It is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimates. The superior performance of this new algorithm is demonstrated through simulation examples.

143 citations

Journal ArticleDOI
TL;DR: This paper provides a complete analysis of the formation stability for this class of decentralized control problems and relates the complete closed-loop system poles to the transmitter and receiver gains, and the spectral properties of the Laplacian of the graph describing the communication links within the formation.
Abstract: The control of cooperative formations of vehicles can be based on parallel estimation, where each vehicle determines its control action from a locally maintained estimate of the entire observable formation state. Vehicles may communicate with one another allowing the local estimates to incorporate information from other estimators in the formation. This paper studies the dynamics that arise in this situation and provides a complete analysis of the formation stability for this class of decentralized control problems. In the absence of communication, the local estimator-controllers' open-loop dynamics necessarily appear in the closed-loop system dynamics, giving a more stringent closed-loop stability condition than in the single controller case. The estimators achieve consensus if and only if the controllers' open-loop dynamics are stable. Communication amongst the estimators can be used to specify the complete system dynamics and we present a framework for the analysis and design of communicated information links in the formation. We relate the complete closed-loop system poles to the transmitter and receiver gains, and the spectral properties of the Laplacian of the graph describing the communication links within the formation. These results also apply to parallel estimation problems in other applications including power system control and redundant channel control architectures.

143 citations

Journal ArticleDOI
TL;DR: A neural-based adaptive observer is introduced for state estimation as well as system identification using only output measurements during online operation via the online approximation of a priori unknown functions.
Abstract: This paper extends the application of neurocontrol approaches to a new class of nonlinear systems diffeomorphic to output feedback nonlinear systems with unmeasured states. A neural-based adaptive observer is introduced for state estimation as well as system identification using only output measurements during online operation. System identification is achieved via the online approximation of a priori unknown functions. The controller is designed using the backstepping control design procedure. Leakage terms in the adaptive laws and nonlinear damping terms in the backstepping controller are introduced to prevent instability from arising due to the inherent approximation error. A primary benefit of the online function approximation is the reduction of approximation errors, which allows reduction of both the observer and controller gains. A semi-global stability analysis for the proposed approach is provided and the feasibility is investigated by an illustrative simulation example.

143 citations

Journal ArticleDOI
TL;DR: In this paper, the identification, estimation and diagnostic checking of closed-loop systems is discussed and illustrated on two real sets of data, i.e., data generated by a process industry.
Abstract: In the process industries data must often be obtained under conditions of closedloop operation; that is, under conditions where feedback control is being applied. In the analysis of such data care is needed to properly take account of the manner of its generation. In particular, if standard open-looped procedures of model identification, estimation and diagnostic checking are applied to closed-loop data incorrect models niay result and lack of fit not be detected. This paper discusses the identification, estimation and diagnostic checking of closedloop systems and illustrates the ideas on two real sets of data.

142 citations

Journal ArticleDOI
TL;DR: The authors generalize linear subspace Identification theory to an analog theory for the subspace identification of bilinear systems and shows that most of the properties of linear sub space identification theory can be extended to similar properties for bilinears systems.
Abstract: The authors generalize linear subspace identification theory to an analog theory for the subspace identification of bilinear systems. A major assumption they make is that the inputs of the system should be white and mutually independent. It is shown that in that case most of the properties of linear subspace identification theory can be extended to similar properties for bilinear systems. The link between the presented bilinear subspace method and Kalman filter theory is made. Finally, the practical relevance of the method is illustrated by making a direct comparison between linear and bilinear subspace identification methods when applied on data from a model of a distillation column.

142 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023177
2022361
2021646
2020813
2019804
2018862