An improved algorithm for identification of time varying parameters using recursive digital techniques

Abstract

Identification is the process of determining values for the characteristic quantities, called parameters, of a system. Examples of such quantities are mass, inductance, resistance, spring coefficient, gain, et cetera. The decreasing cost of digital processors and the versatility of digital programming make digital methods an attractive means of accomplishing identification. It is important, however, that an identifier be able to track any change in a parameter if its output is to be used in any predictive capacity, such as in an adaptive controller. Most studies of digital identification have avoided the topic of time variations by using batch processing methods that implicitly assume constant parameters; this thesis does not.

This thesis first investigates the parameter-tracking capabilities of a popular, real-time digital identification algorithm, the recursive weighted least squares method. This method is claimed to be able to track only slowly time-varying parameters. Based on the results of this study, a method of improving the accuracy of estimates of time-varying parameters is developed. This method, called conditioning, is a post-processor to the recursive weighted least squares algorithm. The results of tests of this method using three different plant simulations are presented, demonstrating the improved accuracy achieved by conditioning estimates of time-varying parameters.