With the escalating demand for fast simulation of large-scale multi-input multi-output (MIMO) RCS circuits formulated as second-order differential systems, the need arises for more effective decentralized second-order model order reduction (MOR) methods, while providing a desired approximation of the original system. Dynamic relative gain array (DRGA) that takes into account both the steady-state and dynamic system information has shown promising efficacy in measuring the degree of each loop interaction, which is crucial for decoupling a MIMO system into several multi-input single-output (MISO) subsystems. Although several decentralized MOR methods have been introduced for dimension reduction to linear MIMO networks, hardly has any research explored second-order decentralized MOR methods with regard to MIMO RCS circuits. Besides, the existing DRGA method based on first-order state feedback predictive control greatly increases the computational complexity when directly applying to second-order RCS systems. Hence, we develop a second-order block Arnoldi method based on DRGA, termed DRGA-SOBAR, which enables the extension of the SOAR method and the second-order DRGA method to MIMO scenarios. Experimental results on RCS networks show that most input-output interactions are negligible in terms of the magnitude-wise insignificance, and our proposed DRGA-SOBAR based reduced systems perform with higher accuracy compared to the PRIMA and the generalized block SOAR (SOBAR) methods, and higher efficiency compared to the decentralized SOBAR algorithm based on RGA method as well.

中文翻译：

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基于 DRGA 的 MIMO RCS 电路模型降阶二阶块 Arnoldi 方法