Accession Number : AD0718995

Title :   Discrete Model Identification Based on Correlation Functions.

Descriptive Note : Technical rept.,

Corporate Author : LOUISIANA STATE UNIV BATON ROUGE DEPT OF CHEMICAL ENGINEERING

Personal Author(s) : Froisy,Brian ; Smith,Cecil ; Corripio,Armando

Report Date : JAN 1971

Pagination or Media Count : 25

Abstract : Using most of the techniques currently available, some information concerning the dynamics of a system must be known before any meaningful control strategy can be implemented. This information can be presented in the form of a plant model which may be obtained in a variety of ways, ranging from a model derived from knowledge of the basic physical phenomena involved to some simple empirical model (e.g., first-order lag with dead time). In this paper, a technique of obtaining a dynamic plant model for a general system is presented and applied to two specific cases. The identification technique discussed in this paper produces a discrete model, and as such should be useful in a digital control environment. The basic approach of the technique is to apply a straight-forward multiple linear regression to points on the discret auto- and cross-correlation functions calculated from a system's sampled experimental input-output record. (Author)

Descriptors :   (*CONTROL SYSTEMS, MATHEMATICAL MODELS), REGRESSION ANALYSIS, LEAST SQUARES METHOD, DIFFERENTIAL EQUATIONS, PROBABILITY, WHITE NOISE, STATISTICAL FUNCTIONS, ADAPTIVE CONTROL SYSTEMS

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE