Subspace identification method incorporating prior information

Abstract

Subspace identification methods proved to be a powerful tool, which can further benefit from the incorporation of prior information. In the industrial environment, there is often strong prior information about the identified system, that can be used to improve the model quality and its compliance with physical reality. Such prior information can be the known static gains, the dominant time constants, the impulse response smoothness, etc. An idea comes from the possibility to consider the subspace identification as an optimization problem of finding a model with the optimal multi-step predictions on the experimental data. Further, the problem is reformulated to the Bayesian framework allowing to combine available prior information with the information contained in the experimental data by covariance matrix shaping. The paper is completed with an application to experimental data from an oil firing steam boiler with the rated effective power of 100 MW.