Subspace like identification incorporating prior information

Abstract

The subspace identification methods have proved to be a powerful tool, which can further benefit from the prior information incorporation algorithm proposed in this note. In the industrial environment, there is often some knowledge about the identified system (known static gains, dominant time constants, low frequency character, etc.), which can be used to improve model quality and its compliance with first principles. The proposed algorithm has two stages. The first one is similar to the subspace methods as it uses their interpretation as an optimization problem of finding parameters of an optimal multi-step linear predictor for the experimental data. This problem is reformulated in the Bayesian framework allowing prior information incorporation in the form of the mean value and the covariance of the impulse response, which is shown to be useful for the incorporation of several prior information types. The second stage with state space model realization from the posterior impulse response estimate is different from the standard subspace methods as it is based on the structured weighted lower rank approximation, which is necessary to preserve the prior information incorporated in the first stage.