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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26 Dec 2018TL;DR: The paper gives a survey of errors-in-variables methods in system identification, and a number of approaches for parameter estimation of errors invariables models are presented.
Abstract: The paper gives a survey of errors-in-variables methods in system identification. Background and motivation are given, and examples illustrate why the identification problem can be difficult. Under general weak assumptions, the systems are not identifiable, but can be parameterized using one degree-of-freedom. Examples where identifiability is achieved under additional assumptions are also provided. A number of approaches for parameter estimation of errors-in-variables models are presented. The underlying assumptions and principles for each approach are highlighted.
440 citations
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TL;DR: This article gives a survey of basic techniques to derive and analyse algorithms for tracking time-varying systems, with special attention to the study of how different assumptions about the true system's variations affect the algorithm.
438 citations
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01 Mar 2015
TL;DR: This valuable volume offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology and addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis.
Abstract: This valuable volume offers a systematic approach to flight vehicle system identification and covers exhaustively the time-domain methodology. It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case are provided. The book also presents data gathering and model validation and covers both large-scale systems and high-fidelity modeling.
Real world problems dealing with a variety of flight vehicle applications are addressed and solutions are provided. Examples encompass such problems as estimation of aerodynamics, stability, and control derivatives from flight data, flight path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, and other critical considerations.
Beginners, as well as practicing researchers, engineers, and working professionals who wish to refresh or broaden their knowledge of flight vehicle system identification, will find this book highly beneficial. Based on years of experience, the book also provides recommendations for overcoming problems likely to be faced in developing complex nonlinear and high-fidelity models and can help the novice negotiate the challenges of developing highly accurate mathematical models and aerodynamic databases from experimental flight data.
Software that runs under MATLAB® and sample flight data are provided to assist the reader in reworking the examples presented in the text. The software can also be adapted to the reader’s own interests.
435 citations
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01 Mar 1994TL;DR: The algorithm is divided into two phases, a dynamical neural network identifier is employed to perform "black box" identification and then a dynamic state feedback is developed to appropriately control the unknown system.
Abstract: In this paper, we are dealing with the problem of controlling an unknown nonlinear dynamical system. The algorithm is divided into two phases. First a dynamical neural network identifier is employed to perform "black box" identification and then a dynamic state feedback is developed to appropriately control the unknown system. We apply the algorithm to control the speed of a nonlinearized DC motor, giving in this way an application insight. In the algorithm, not all the plant states are assumed to be available for measurement. >
435 citations
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TL;DR: In this paper, the authors present various applications of neural networks in energy problems in a thematic rather than a chronological or any other order, including modeling the heat-up response of a solar steam-generating plant, estimation of a parabolic trough collector intercept factor, and the estimation of the local concentration ratio.
431 citations