Integrating Prior Information into Subspace Identification Methods

Abstract

Integrating prior information into subspace identification methods improves their usability for industrial data, where experimental data by them self are in many cases not good enough to give a proper model. The identification experiments in the industrial environment are limited by the economical and safety reasons. However, in practical applications, there is often strong prior information about the identified system, which can be exploited in the identification. The presented algorithm formulates subspace identification as a multi-step predictor optimization. Reformulation to the Bayesian framework allows to incorporate prior information. The paper is completed with the application to the experimental data from the oil burning steam boiler with the rated power of 100 MW.