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Strictly Positive Real (SPR) transfer functions arise in many areas ofengineering like passivity theory in circuit analysis and adaptive control toname a few. In many physical systems, it is possible to conclude that thesystem is Positive Real (PR) or SPR but system identification algorithms mightproduce estimates which are not SPR. In this paper, an algorithm to approximatefrequency response data with SPR transfer functions using GeneralizedOrthonormal Basis Functions (GOBFs) is presented. Prior knowledge of the systemhelps us to get approximate pole locations, which can then be used to constructGOBFs. Next, a convex optimization problem will be formulated to obtain anestimate of the SPR transfer function.

Data-Driven Strictly Positive Real System Identification with prior System Knowledge