Adaptive frequency response identification

Parker, Philip J.; Bitmead, Robert R.

doi:10.1109/cdc.1987.272820

Cited by 90 publications

(17 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider the output error (32) with , and the idea is to use this output error for estimating the second-order statistics of the noise process . Next denote (33) and consider the following estimate for : (34) where is a (positive real-valued) window function, similar to the ones used in spectral analysis [26]. Now the following Theorem can be established for the situation that we are dealing with open loop measurements.…”

Section: B Estimation Of Noise Auto-covariance Functionmentioning

confidence: 99%

“…First, 500 samples of the output have been measured while the input is zero. This is a so-called free-run experiment and is used to estimate according to (33). This estimate is used later in (34) for the construction of an overbounded estimate of to be used in the uncertainty bounding.…”

Section: Simulation Examplementioning

confidence: 99%

“…The worst case deterministic type of problem has been addressed in terms of frequency response data in [5], [10], [17], [18], [25], [33], and [34]. In the case of time-domain data, a deterministic/worst case approach is often referred to as set-membership identification or as parameter bounding identification.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Identification of probabilistic system uncertainty regions by explicit evaluation of bias and variance errors

Hakvoort

Hof

1997

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-A procedure is developed for identification of probabilistic system uncertainty regions for a linear time-invariant system with unknown dynamics, on the basis of time sequences of input and output data. The classical framework is handled in which the system output is contaminated by a realization of a stationary stochastic process. Given minor and verifiable prior information on the system and the noise process, frequency response, pulse response, and step response confidence regions are constructed by explicitly evaluating the bias and variance errors of a linear regression estimate. In the model parameterizations, use is made of general forms of basis functions. Conservatism of the uncertainty regions is limited by focusing on direct computational solutions rather than on closed-form expressions. Using an instrumental variable method for identification, the procedure is suitable also for input-output data obtained from closed-loop experiments.

show abstract

Section: B Estimation Of Noise Auto-covariance Functionmentioning

confidence: 99%

Section: Simulation Examplementioning

confidence: 99%

See 1 more Smart Citation

Identification of probabilistic system uncertainty regions by explicit evaluation of bias and variance errors

Hakvoort

Hof

1997

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…The FSF structure was first introduced into the field of system identification and automatic control by Bitmead and Anderson [9], Parker and Bitmead [10].…”

Section: Modelling Ofmentioning

confidence: 99%

MIMO Frequency Sampling Filters for Modelling of MIMO System Applications

Aziz

Mohd‐Mokhtar

2013

j.eng.technol.sci.

View full text Add to dashboard Cite

Abstract. In the modelling of a system based on approach, data acquisition is the first procedure that must be data acquisition process from data. This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance high frequency region. In addition, bias estimatio considers direct identification from a closed problem, in this paper a introduced. Multi-input obtain only relevant model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling filters was demonstrated by process data and steam generator plant data. raw data was successfully compressed and identification purposes with less computational time used to develop a model of the system, analysis, and also for developing a new Keywords: Data compression parametric model; system identification. IntroductionSystem identification is based on study and analysis of input and output data collected from a system. modelling of a system based on a system identification approach, data acquisition is the first procedure that must be carried out.acquisition process from a real system typically yields large amounts of This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance, especially in high frequency region. In addition, bias estimation will occur if one only considers direct identification from a closed-loop system. To overcome in this paper a multivariable frequency sampling filter approach is input-multi-output (MIMO) raw data are analyzed in order to and meaningful parameters that describe the empirical model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling demonstrated by compressing two sets of data: pH neutralization process data and steam generator plant data. The results show that the amount of successfully compressed and that the output was ready for identification purposes with less computational time, i.e. they could be further used to develop a model of the system, to conduct time and frequency response developing a new control system design.compression; frequency sampling filters; multivariable process system identification.System identification is based on study and analysis of input and output data system. To perform this procedure, a data acquisition process is unavoidable. Data acquisition from a real system typically yields large amounts time data. This may lead to unacceptable computational time during the identification process. Also, the raw data may contain complex system an extended version of the paper published by the authors in Int. Conf. Modelling ofElectronic Engineering, Universiti Sains Malaysia, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, system identification carried out. The real system typically yields large amounts of This may lead to unacceptable computational time during ...

show abstract

“…Motivated by the features of the Kalman-filter-based short-time DFT identification routines proposed in (Parker and Bitmead, 1987), (Nevaranta et al, 2015b) for frequency response identification of open-loop and closed-loop systems using a multi-sine excitation signal, the objective of this paper is to study the same routine in the case of a swept chirp excitation signal. In particular, the main idea of using the Kalman filter for estimating time-varying signals in a complex form is considered, but here the knowledge of the time-varying frequency of the excitation signal is used to update Kalman gains.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Nevaranta

Derammelaere

Parkkinen

et al. 2016

MIC

View full text Add to dashboard Cite

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

show abstract

Adaptive frequency response identification

Cited by 90 publications

References 10 publications

Identification of probabilistic system uncertainty regions by explicit evaluation of bias and variance errors

Identification of probabilistic system uncertainty regions by explicit evaluation of bias and variance errors

MIMO Frequency Sampling Filters for Modelling of MIMO System Applications

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Contact Info

Product

Resources

About