RGA-SOBAR: Second-Order Block Arnoldi Method Based on Relative Gain Array for Model Order Reduction of MIMO RCS Circuits

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. Relative gain array (RGA) 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. We develop a second-order block Arnoldi method based on RGA, termed RGA-SOBAR, which enables the extension of the SOAR 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 RGA-SOBAR based reduced systems perform with higher accuracy compared to the PRIMA and the generalized block SOAR methods.