Abstract
We present a framework for passivity-preserving model reduction for RLC systems that includes, as a special case, the well-known PRIMA model reduction algorithm. This framework provides a new interpretation for PRIMA, and offers a qualitative explanation as to why PRIMA performs remarkably well in practice. In addition, the framework enables the derivation of new error bounds for PRIMA-like methods. We also show how the framework offers a systematic approach to computing reduced-order models that better approximate the original system than PRIMA, while still preserving passivity.
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