Subspace Identification Method Incorporating Prior Information

Subspace Identification Method Incorporating Prior Information

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Unformatted text preview: Subspace Identification Method Incorporating Prior Information Pavel Trnka and Vladim r Havlena 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. I. INTRODUCTION The favorable properties of Subspace State Space System IDentification (4SID) have shown their suitability for the industrial applications [1]. Mainly their numerical robustness and ability to identify MIMO (Multiple Inputs Multiple Outputs) systems with the same complexity as for SISO (Single Input Single Output) systems without the need for an extensive structural parameterization. However, it is quite usual, that the input/output data acquired from the identification experiments do not have the sufficient quality to give a good model. This may be caused by the fact, that the identification experiments in the indus- trial environment are limited by the economical and safety reasons, which results into data without proper excitation and with strong noise contamination. The black-box approach, such as in 4SID, relying only on the experimental data, may fail in such cases. In the practical applications there is often strong prior information about the system, which can be exploited by the identification algorithm to improve the identified model quality. Such information can be: an approximate knowledge of time constants, the known static gains, an integrating character, step response smoothness etc. Incorporation of prior information into 4SID methods will be addressed in this article. The previous works on subspace identification and prior information incorporation were directed to the specific cases [2], [3]. A more general solution is still not available. In This work was supported by financial support of the Grant Agency of Czech Republic under grant No. 102/05/0271 and 102/05/2075....
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This note was uploaded on 01/29/2011 for the course ENGR 52 taught by Professor Mcmillan during the Spring '10 term at Baylor Med.

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Subspace Identification Method Incorporating Prior Information

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