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recursive identification under scarce measurements-convergence analysis

AUTOMATICA 2002年第3期38卷 535-544页

作者： Sanchis, R Albertos, P Univ Politecn Valencia DISA E-46071 Valencia Spain Univ Jaume 1 Dept Technol Castellon de La Plana 12071 Spain

In this paper, the problem of recursive identification under scarce-data operation is addressed. The control action is assumed to be updated at a fixed rate, while the output is assumed to be measured synchronously with the input update, but with an irregular availability pattern. Under these conditions the use of pseudo-linear recursive algorithms is studied. The main result is the convergence analysis for the case of regular but scarce data availability. The existence of wrong attractors is demonstrated, and a local stability condition of the identification algorithm is derived. (C) 2002 Published by Elsevier Science Ltd.

关键词： missing-data unconventional sampling pseudo-linear recursive identification convergence analysis

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Adaptive extended Kalman filter for recursive identification under missing data 49

Adaptive extended Kalman filter for recursive identification...

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49th IEEE Conference on Decision and Control (CDC)

作者： Penarrocha, I. Sanchis, R. Univ Jaume 1 Dept Engn Sistemes Ind & Disseny Castellon de La Plana Spain

ISBN: (纸本)9781424477463

In this work, the parameter identification for systems with scarce measurements is addressed. A linear plant is assumed and its output is assumed to be available only at sporadic instants of time and affected by noise measurement. The identification is carried out estimating the missing outputs in order to construct the regression vector needed by the parameter estimation algorithm and using the available output information not only to update the estimated parameter vector, but also to update the regression vector in order to fasten the convergence of the algorithm. The problem is addressed with an adaptive extended Kalman filter that estimates and correct both the parameters and the regression vector, allowing to improve the convergence speed of the algorithm with respect to other existing ones on the literature as it is shown with several examples.

关键词： Parameter estimation Output estimation Least squares Randomly missing outputs Kalman filter Networked Control Systems pseudo-linear recursive identification Algorithm initialization

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Initializing Parameter Estimation Algorithms Under Scarce Measurements

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IFAC Proceedings Volumes 2003年第16期36卷 1897-1902页

作者： P. Albertos R. Sanchis I. Peñarrocha Departamento de Ingeniería de Sistemas y Automática (DISA) Universidad Politécnica de Valencia Apdo. 22012 E-46071 Valencia Spain. Departament de Tecnologia Universitat Jaume I Campus de Riu Sec 12071 Castellón Spain

In this paper, the problem of initializing identification algorithms with non-regular sampling is addressed. When a recursive identification algorithm is used to estimate the parameters, the convergence of the parameters is affected by the existence of wrong attractors. The initialization of the algorithm is studied in different situations. First, the algorithm starts without past information about the model parameters. An interpolation method is used to estimate the missing data. If a change of the control action updating rate is planned, the new model parameters are initialized by estimations obtained either by interpolation (if the periods are multiple) or by approximate δ modelling using the measurements taken under current operating conditions. Some examples illustrate the attractors avoidance and some conclusions are drafted.

关键词： Missing-Data Unconventional Sampling pseudo-linear recursive identification Algorithm Initialization Convergence Analysis Delta Operator

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recursive identification under Scarce Measurements

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IFAC Proceedings Volumes 2000年第15期33卷 745-750页

作者： R. Sanchis P. Albertos Unitat Predepartamental de Tecnologia Universitat Jaume I Campus de Riu Sec 12071 Castellon Spain Departamento de Ingeniería de Sistemas y Automática (DISA) Universidad Politécnica de Valencia Apdo. 22012 E-46071 Valencia Spain

关键词： Missing-Data Unconventional Sampling pseudo-linear recursive identification

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Adaptive extended Kalman filter for recursive identification under missing data

Adaptive extended Kalman filter for recursive identification...

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2010 49th IEEE Conference on Decision and Control

作者： I. Penarrocha R. Sanchis Departament d'Enginyeria de Sistemes Industrials i Disseny Universitat Jaume I Castello Spain

ISBN: (纸本)9781424477456

关键词： Parameter estimation Output estimation Least squares Randomly missing outputs Kalman filter Networked Control Systems pseudo-linear recursive identification Algorithm initialization

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