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
More filters
Book•
13 Oct 2005
TL;DR: Continuous Control Systems: A Review -- Computer Control Systems -- Robust Digital Controller Design Methods -- Design of Digital Controllers in the Presence of Random Disturbances -- System Identification: The Bases.
Abstract: Continuous Control Systems: A Review -- Computer Control Systems -- Robust Digital Controller Design Methods -- Design of Digital Controllers in the Presence of Random Disturbances -- System Identification: The Bases -- System Identification Methods -- Practical Aspects of System Identification -- Practical Aspects of Digital Control -- Identification in Closed Loop -- Reduction of Controller Complexity.
371 citations
TL;DR: The aim of the paper is to show that, using the obtained ensemble of data, POD can be used to model accurately the natural convection and this approach is very efficient in the sense that it uses the smallest possible number of parameters, and thus, is suited for process control.
371 citations
TL;DR: It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes.
Abstract: This paper outlines how it is possible to decompose a complex non-linear modelling problem into a set of simpler linear modelling problems. Local ARMAX models valid within certain operating regimes are interpolated to construct a global NARMAX (non-linear NARMAX) model. Knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models, hence it should not be considered as a pure black-box approach, but as an approach that utilizes a limited amount of a priori system knowledge. It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes. Standard system identification algorithms can be used to identify the NARMAX model, and several aspects of the system identification problem are discussed and illustrated by a simulation example.
370 citations
TL;DR: The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data?
Abstract: The gap between the models used in control synthesis and those obtained from identification experiments is considered by investigating the connection between uncertain models and data. The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data? This problem is studied for the standard H/sub infinity // mu framework models. A necessary condition for such a model to describe an experimental datum is obtained. For a large class of models in the robust control framework, this condition is computable as the solution of a quadratic optimization problem. >
368 citations
TL;DR: The theoretical and computational issues arising in the selection of the optimal sensor configuration for parameter estimation in structural dynamics are addressed and two algorithms are proposed for constructing effective sensor configurations that are superior in terms of computational efficiency and accuracy to the sensor configurations provided by genetic algorithms.
367 citations