Real time recursive block parameter estimation of second order systems

Goodwin, C.K., 1997. Real time recursive block parameter estimation of second order systems. PhD, Nottingham Trent University.

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Many real world dynamic systems can be approximated well using second order systems. It is often required, therefore, in engineering and other situations to determine the characterizing parameters of observed data, with the assumption that the data represents a second order system.

This study investigates the parameter estimation problem encompassing a wide range of techniques and algorithms. Conventional approaches are tested and in some cases combined to produce hybrid algorithms. Two novel methods are also applied, and compared with the other techniques. These novel methods are neural networks and genetic algorithms.

Further, a new algorithm is proposed which is applied to all techniques tested. This new algorithm adaptively adjusts the sampling frequency at which observed data is read, based on previous estimates of the parameters. It is shown that this improves the accuracy of the parameter estimation process.

A complete simulation environment is devised enabling parameter estimation to be tested under a range of situations. Firstly, when the system parameters are constant with time. Then secondly, when the parameters vary through the time period of the observed data. The simulation enables the parameters to be estimated in blocks of data. Further enhancement of the algorithms enable them to perform recursively, taking account of previous block's estimates. Finally, all algorithms are tested on their tolerance to two types of noise. The complete simulation allows recursive block parameter estimation which adaptively varies the sampling frequency to increase the accuracy of the estimation, under a range of noise conditions.

Item Type: Thesis
Creators: Goodwin, C.K.
Date: 1997
ISBN: 9781369325027
Rights: This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author, and that no quotation from the thesis and no information derived from it may he published without the author’s prior written consent.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 25 Jun 2021 08:31
Last Modified: 01 Nov 2023 14:40

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