Jens G. Linden

Frisch scheme identification for dynamic diagonal bilinear models

The article addresses the problem of dynamic system identification in the errors-in-variables framework for a class of discrete-time time-invariant input–output bilinear models when subjected to a white input signal. The proposed algorithm is based on an extension of the bias-compensated least squares method and utilises the Frisch scheme equations to determine the parameter vector together with the variances of the input and output noise sequences. The appropriateness of the approach is analysed and its performance evaluated when compared to other errors-in-variables identification techniques by means of a Monte Carlo simulation. The results obtained demonstrate the accuracy of the proposed method and the performance in terms of noise robustness is also observed.

Algorithms for recursive/semi-recursive bias-compensating least squares system identification within the errors-in-variables framework

Algorithms for the recursive/semi-recursive estimation of the system parameters as well as the measurement noise variances for linear single-input single-output errors-in-variables systems are considered. Approaches based on three offline techniques are presented: namely, the bias eliminating least squares, the Frisch scheme and the extended bias compensating the least squares method. Whilst the underlying equations used within these approaches are identical under certain design choices, the performances of the recursive/semi-recursive algorithms are investigated via simulation, in order to determine the most suitable technique for practical applications.

Gradient-Based Approaches for Recursive Frisch Scheme Identification

Abstract An algorithm for recursive Frisch scheme system identification of linear single-input single-output errors-in-variables systems is developed. For the update of the estimated model parameters, a recursive bias-compensating least squares algorithm, which is based on the well-known recursive least squares technique, is considered. The estimate of the output measurement noise variance is determined using a conjugate gradient method, which tracks the smallest eigenvalue of a slowly varying matrix. For the update of the input measurement noise estimate, a steepest gradient search is applied. It tracks the minimum of a model selection cost function, which is based on a set of high order Yule-Walker equations.

Regularized Structured Total Least Norm for the Identification of Bilinear Systems in the Errors-in-Variables Framework

The paper addresses the identification of time-invariant bilinear system (BS) models in the errors-in-variables (EIV) framework. The proposed scheme is based on the structured total least norm (STLN) technique extended here to handle BS. The performance of the presented approach, i.e. the bilinear STLN (BSTLN) with its further extension incorporating the Tikhonov regularization is compared to several other EIV identification techniques via an extensive Monte-Carlo simulation study. The results obtained demonstrate a considerable noise robustness and therefore the applicability of the BSTLN algorithm in the EIV framework.

23rd IAR* International Workshop: Advanced Control and Diagnosis (ACD)

The 23rd IAR International Workshop on Advanced Control and Diagnosis (ACD), referred to here as the IAR-ACD, was held at Coventry University in the UK, on the 27th-28th November 2008, and was organised by the Control Theory and Applications Centre (CTAC) a multidisciplinary research centre based within the Department of Mathematics, Statistics and Engineering Science in the Faculty of Engineering and Computing. The annual Workshop especially aims to promote young researchers and students and operates, therefore, as a low cost break-even event. Following the tradition of the previous years, a remarkably low fee of £130 and a reduced student fee of £100 attracted around 120 participants from three different continents. The 23rd IAR-ACD was the first ever workshop in the series to be held outside France and/or Germany where the IAR network was originally established. For over two decades the network comprised four French and two German institutions, these were specific university research laboratories based in Grenoble (FR), Mulhouse (FR), Nancy (FR), Strasbourg (FR), Duisburg-Essen (DE) and Karlsruhe (DE). Recently, in 2006 when Coventry University became the 7th member of the network, a decision was taken to rename the IAR recognising it as a truly European network, thus the European (EU) IAR was formed. It was also agreed at that time that Coventry University would plan and host the 23rd Workshop in 2008. The network increased again in 2007, when the University of Zelena Gora (PL) became the 8th member of the EU IAR. *EU IAR: European (EU) Institute for Applied Research (IAR)