A linear estimation algorithm for ARMAX models with time dependent coefficients

An approach that models a nonlinear process based on multi-input/single-output measurements is developed. The approach uses a stochastic time-varying autoregressive moving average models that incorporate a number of exogeneous measurable inputs (TARMAX). The TARMAX model coefficients are explicit fu...

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Bibliographic Details
Published inProceedings of the 1999 American Control Conference (Cat. No. 99CH36251) Vol. 1; pp. 689 - 693 vol.1
Main Authors Mrad, R.B., Farag, E., Levitt, J.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1999
Subjects
Autoregressive processes
Computational complexity
Inverse problems
Microcomputers
Milling
Numerical simulation
Polynomials
Predictive models
Stochastic processes
Transfer functions
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Summary:An approach that models a nonlinear process based on multi-input/single-output measurements is developed. The approach uses a stochastic time-varying autoregressive moving average models that incorporate a number of exogeneous measurable inputs (TARMAX). The TARMAX model coefficients are explicit functions of time and are expressed as a linear combination of a set of pre-selected functions. The modeling approach is shown to be suitable to a milling process and a strictly linear method for evaluating the TARMAX model coefficients is presented. The model estimation approach does not require initial guess parameter values and is suitable for microcomputer implementation. The performance of the estimation algorithm is verified through numerical simulation examples.
ISBN:9780780349902
0780349903
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.1999.782915