Topic
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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TL;DR: In this paper, the identification problem of linear dynamical systems is considered and the identiability of such an arbitrary parametrization is considered in several situations, assuming that the transfer function can be identified asymptotically, conditions are derived for local and global identifiability.
Abstract: We consider the problem of what parametrizations of linear dynamical systems are appropriate for identification (i.e., so that the identification problem has a unique solution, and all systems of a particular class can be represented). Canonical forms for controllable linear systems under similarity transformation are considered and it is shown that their use in identification may cause numerical difficulties, and an alternate approach is proposed which avoids these difficulties. Then it is assumed that the system matrices are parametrized by some unknown parameters from a priori system knowledge. The identiability of such an arbitrary parametrization is then considered in several situations. Assuming that the system transfer function can be identified asymptotically, conditions are derived for local and global identifiability. Finally, conditions for identifiability from the output spectral density are given for a system driven by unobserved white noise.
289 citations
TL;DR: Using some recent asymptotic expressions for the bias and the variance of the estimated transfer function, it is shown how this performance degradation can be minimized by a proper experiment design.
287 citations
TL;DR: In this article, the Fisher information matrix is used to locate sensors in a dynamic system so that data acquired from those locations will yield the best estimates of the parameters that need to be identified.
Abstract: This paper provides a methodology for optimally locating sensors in a dynamic system so that data acquired from those locations will yield the best identification of the parameters to be identified. It addresses the following questions: (1) Given m sensors, where should they be placed in a spatially distributed dynamic system so that data from those locations will yield best estimates of the parameters that need to be identified?; and (2) given that we have already installed p sensors in a dynamic system, where should the next s be located? The methodology is rigorously founded on the Fisher information matrix and is applicable to both linear and nonlinear systems. A rapid algorithm is provided for use in large multi‐degree‐of‐freedom systems. After developing the general methodology, the paper goes on to develop the method in detail for a linear N‐degree‐of‐freedom, classically damped, system. Numerical examples are provided and it is verified that the optimal placement of sensors, as dictated by the met...
286 citations
TL;DR: This paper deals with the application of fractional system identification to lead acid battery state of charge estimation and a new fractional model of the battery is proposed based on parameter variations of this model.
285 citations