Interpolation-based ℌ2 model reduction for port-Hamiltonian systems

Port network modeling of physical systems leads directly to an important class of passive state space systems: port-Hamiltonian systems. We consider here methods for model reduction of large scale port-Hamiltonian systems that preserve port-Hamiltonian structure and are capable of yielding reduced order models that satisfy first-order optimality conditions with respect to an H2 system error metric. The methods we consider are closely related to rational Krylov methods and variants are described using both energy and co-energy system coordinates. The resulting reduced models have port-Hamiltonian structure and therefore are guaranteed passive, while still retaining the flexibility to interpolate the true system transfer function at any (complex) frequency points that are desired.

[1]  Steven J. Cox,et al.  Low-dimensional, morphologically accurate models of subthreshold membrane potential , 2009, Journal of Computational Neuroscience.

[2]  Angelika Bunse-Gerstner,et al.  h2-norm optimal model reduction for large scale discrete dynamical MIMO systems , 2010, J. Comput. Appl. Math..

[3]  R. Skelton,et al.  Covariance Equivalent Realizations with Application to Model Reduction of Large-Scale Systems , 1985 .

[4]  U. Viaro,et al.  Rational L/sub 2/ approximation: a non-gradient algorithm , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[5]  Axel Ruhe Rational Krylov algorithms for nonsymmetric eigenvalue problems. II. matrix pairs , 1994 .

[6]  Yoram Halevi,et al.  Frequency weighted model reduction via optimal projection , 1992 .

[7]  Arthur E. Bryson,et al.  Second-order algorithm for optimal model order reduction , 1990 .

[8]  James Lam,et al.  An approximate approach to H2 optimal model reduction , 1999, IEEE Trans. Autom. Control..

[9]  A. Schaft L2-Gain and Passivity Techniques in Nonlinear Control. Lecture Notes in Control and Information Sciences 218 , 1996 .

[10]  Robert Skelton,et al.  Model reductions using a projection formulation , 1987, 26th IEEE Conference on Decision and Control.

[11]  Paul Van Dooren,et al.  H2-optimal model reduction of MIMO systems , 2008, Appl. Math. Lett..

[12]  D. Wilson Optimum solution of model-reduction problem , 1970 .

[13]  Arjan van der Schaft,et al.  Structure preserving model reduction of port-Hamiltonian systems by moment matching at infinity , 2010, Autom..

[14]  L. Meier,et al.  Approximation of linear constant systems , 1967, IEEE Transactions on Automatic Control.

[15]  R. Skelton,et al.  Linear system approximation via covariance equivalent realizations , 1985 .

[16]  van der Arjan Schaft,et al.  Structure preserving port-Hamiltonian model reduction of electrical circuits , 2011 .

[17]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[18]  Eric James Grimme,et al.  Krylov Projection Methods for Model Reduction , 1997 .

[19]  Serkan Gugercin,et al.  H2 Model Reduction for Large-Scale Linear Dynamical Systems , 2008, SIAM J. Matrix Anal. Appl..

[20]  L. Watson,et al.  Contragredient Transformations Applied to the Optimal Projection Equations , 1992 .

[21]  A. Antoulas,et al.  A Rational Krylov Iteration for Optimal H 2 Model Reduction , 2006 .

[22]  J. Willems Dissipative dynamical systems part I: General theory , 1972 .

[23]  John T. Spanos,et al.  A new algorithm for L2 optimal model reduction , 1992, Autom..

[24]  D. Gaier,et al.  Lectures on complex approximation , 1987 .

[25]  J. Willems,et al.  A behavioral approach to linear exact modeling , 1993, IEEE Trans. Autom. Control..

[26]  Jan G. Korvink,et al.  Oberwolfach Benchmark Collection , 2005 .

[27]  D. Bernstein,et al.  The optimal projection equations for model reduction and the relationships among the methods of Wilson, Skelton, and Moore , 1985 .

[28]  Jan C. Willems,et al.  Dissipative Dynamical Systems , 2007, Eur. J. Control.

[29]  A. Antoulas,et al.  H 2 Model Reduction for Large-scale Linear Dynamical Systems * , 2022 .