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Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

Instrumentation Technical Session I

Tagged Division


Page Count


Page Numbers

23.870.1 - 23.870.8



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Paper Authors


Alireza Rahrooh Daytona State College

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Alireza Rahrooh received B.S., M.S., and Ph.D. degrees in electrical engineering from the University of Akron, Ohio in 1979, 1986, and 1990, respectively. He worked as an Electronic Engineer in Kurdistan from 1979 to 1984. He was involved in conducting sponsored research for the Electrical Power Institute and NASA Lewis Research Center from 1984 to1998. He was appointed to a faculty position in Electrical Engineering at Penn State University in 1988. In 1994, he joined the faculty of Electrical Engineering Technology at UCF till August of 2010 when he moved to Daytona State College. He has presented numerous papers at various conferences and is the author of more than 100 technical articles. His research interests include digital simulation, nonlinear dynamics, chaos, system identification and adaptive control. He is a member of ASEE, IEEE, Eta Kappa Nu, and Tau Beta Pi

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Walter W. Buchanan P.E. Texas A&M University


Remzi Seker Embry-Riddle Aeronautical University

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Dr. Seker is Professor of Electrical, Computer, Software, and Systems Engineering at Embry-Riddle Aeronautical University Daytona Beach
campus. His interest areas are cybersecurity, outreach, and education.

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Linear Model Estimation of Nonlinear Systems Using Least-Squares AlgorithmAbstractThis paper presents a new approach to obtain more accurate linear models of nonlinearsystems using parameter estimation. In particular, a technique is developed to generate anoptimal linear model which is valid over a wide range of trajectories and converges to thedesired steady-state value with no modification of the input using Least-Squares Algorithm.This approach is very efficient and does not require storing the data. Therefore, it can easilybe used and implemented with limited resources for undergraduate curriculum especially inunderdeveloped countries. Most available techniques for linearization of nonlinear systemare only valid about the operating point using “offset derivative”. This technique makes useof the method of finite difference to approximate partial derivatives of a Taylor seriesexpansion of the nonlinear state equations about a nominal operating point or trajectories.The advantages of proposed technique over others are illustrated via obtaining the linearmodels of jet engines nonlinear models.

Rahrooh, A., & Buchanan, W. W., & Seker, R. (2013, June), Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19884

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