Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the basic properties of the singular value decomposition (SVD) for integral equation models of distributed parameter systems (DPS) are presented in the context of process identification and model-based control.
105 citations
TL;DR: The framework for the identification of a dynamic system model based on Gaussian processes is shown, illustrated on a simulated bioreactor example and then applied to two case studies, addressing modelling of the nitrification process in a wastewater treatment plant and biomass growth in the Lagoon of Venice.
Abstract: Different models can be used for nonlinear dynamic system identification and the Gaussian process model is a relatively new option with several interesting features: model predictions contain the measure of confidence, the model has a small number of training parameters and facilitated structure determination, and different possibilities of including prior knowledge exist. In this paper the framework for the identification of a dynamic system model based on Gaussian processes is shown, illustrated on a simulated bioreactor example and then applied to two case studies. The first one addresses modelling of the nitrification process in a wastewater treatment plant and the second models biomass growth in the Lagoon of Venice. Special emphasis is placed on model validation, an often underemphasised part of the identification procedure, where the Gaussian model prediction variance can be utilised.
105 citations
Book•
05 Dec 2003
TL;DR: In this paper, the authors model spatial norms and model reduction model correction spatial control optimal placement of actuators and sensors system identification for spatially distributed systems, and model correction of spatial control.
Abstract: Modelling spatial norms and model reduction model correction spatial control optimal placement of actuators and sensors system identification for spatially distributed systems.
104 citations
TL;DR: In this article, the authors considered the identification of linear systems in the presence of measurement noise which is unknown but bounded in magnitude by some δ > 0 and showed that the optimal identification error is related to the diameter of a so-called uncertainty set and that the latter diameter is upper-bounded by 2δ, if a sufficiently long identification experiment is performed.
104 citations
TL;DR: In this article, the problem of estimating the state vector of a dynamical system from vector measurements when it is known that the vector satisfies norm equality constraints is considered, and the obtained solution is extended to nonlinear measurement models and applied to the spacecraft attitude filtering problem.
Abstract: The problem of estimating the state vector of a dynamical system from vector measurements when it is known that the state vector satisfies norm equality constraints is considered. The case of a linear dynamical system with linear measurements subject to a norm equality constraint is discussed with a review of existing solutions. The norm constraint introduces a nonlinearity in the system for which a new estimator structure is derived by minimizing a constrained cost function. It is shown that the constrained estimate is equivalent to the brute-force normalization of the unconstrained estimate. The obtained solution is extended to nonlinear measurement models and applied to the spacecraft attitude filtering problem.
104 citations