A Behavioral Approach to Passivity and Bounded Realness Preserving Balanced Truncation with Error Bounds

Abstract

In this paper we revisit the problems of passivity and bounded realness preserving model reduction by balanced truncation. In the behavioral framework, these problems can be considered as special cases of balanced truncation of strictly half line dissipative system behaviors, where the number of input variables of the behavior is equal to the positive signature of the supply rate. Instead of input-state-output representations, the balancing algorithm uses normalized driving variable representations of the behavior. We show that the diagonal elements of the minimal solution of the balanced algebraic Riccati equation are the singular values of the map that assigns to each past trajectory the optimal storage extracting future continuation. Since the future behavior is only an indefinite inner product space, the term singular values should be interpreted here in a generalized sense. We establish some new error bounds for this model reduction method.

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