Date of Award

2-1986

Degree Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical Engineering

Supervisor

Professor Naresh K. Sinha

Abstract

The problem of identification of linear multivariable continuous-time systems from input-output data is considered. A survey has been made to present the direct and the indirect approaches in identifying continuous-time systems from the samples of the observations. The direct approach with approximate integration seems to be more promising and hence it is adopted in this work. Three direct methods based on the use of block pulse functions, trapezoidal pulse functions and cubic splines have been compared and applied for multivariable systems. A comprehensive study has been conducted to analyze the effect of noise on the identification. The analysis was carried out first for the single-input single-output case and then extended to the multivariable case. A new approach is presented to overcome the combined effect of the errors in the approximation/and additive white noise on the identification of continuous-time systems. The method consists of modelling the combined error term. Extensive simulations are conducted in order to illustrate the merits of the new procedure. The problem of order determination has been considered and three order determination tests have been studied and applied for continuous-time systems, two of them for the first time as far as the author is aware. The problem of the selection of the structure that will give good conditioned parameterization is also considered. A new procedure to identify the structure in the input-output form is presented. This procedure is suitable for both stationary and non-stationary systems when a change in the structure occurs while the order remains constant. It uses the concept of overlapping parameterization to choose a better conditioned parameterization for the multivariable system whenever ill conditioning is detected. A switching criterion is presented based on the complexity principle which provides a good monitor of the conditioning of the parameterization as well as the suitability of the tested structure.