A Half-Size Singularity Test Matrix for Fast and Reliable Passivity Assessment of Rational Models

One major difficulty in the rational modeling of linear systems is that the obtained model can be nonpassive, thereby leading to unstable simulations. The model's passivity properties are usually assessed by computing the eigenvalues of a Hamiltonian matrix, which is derived from the model parameters. The purely imaginary eigenvalues represent crossover frequencies where the model's conductance matrix is singular, allowing to pinpoint frequency intervals of passivity violations. Unfortunately, the eigenvalue computation time can be excessive for large models. Also, the test applies only to symmetrical models, and the testing is made difficult by numerical noise in the extracted eigenvalues. In this paper a new (non-Hamiltonian) half-size singularity test matrix is derived for use with admittance parameter state-space models, which overcomes these shortcomings. It gives a computational speedup by a factor of eight; it is applicable to both symmetric and unsymmetrical models; and it produces noiseless eigenvalues for reliable passivity assessment.