A novel framework for passive macro-modeling

Passivity enforcement is an important issue for macro-modeling for passive systems from measured or simulated data. Existing convex programming based methods are too expensive and thus are ruled out for realistic application. Other methods based on iteratively fixing the passivity through perturbing the eigenvalues of the Hamiltonian matrix either suffer from convergence issue or lack optimality which will sometimes lead to unacceptable error. In this paper we propose a novel framework for macro-modeling. In addition to the traditional two-stage (fixing plus enforcement) schemes, we propose a post-enforcement optimization, which takes a passive, while potentially not-so-accurate model, as the starting point, and performs local search to find the local optimum with passivity constraint or build-in passivity guarantee. A simple yet stable passive modeling generator is proposed to produce the starting model for optimization. Two algorithms are proposed for performing constrained and unconstrained optimizations. Experiments show that the accuracy of passivity-fixed model can be significantly improved with the proposed methods.