Krylov subspace-based model reduction for a class of bilinear descriptor systems

نویسندگان

  • Mian Ilyas Ahmad
  • Peter Benner
  • Pawan Goyal
چکیده

We consider model order reduction of bilinear descriptor systems using an interpolatory projection framework. Such nonlinear descriptor systems can be represented by a series of generalized linear descriptor systems (also called subsystems) by utilizing the Volterra-Wiener approach [22]. Standard projection techniques for bilinear systems utilize the generalized transfer function of these subsystems to construct an interpolating approximation. However, the resulting reduced-order system may not match the polynomial part of the generalized transfer functions. This may result in an unbounded error in terms of H2 or H∞ norms. In this paper, we derive an explicit expression for the polynomial part of each subsystem by assuming a special structure of the bilinear system which reduces to an index-1 linear DAE if the bilinear term is zero. This allows us to propose an interpolatory technique for bilinear DAEs which not only achieves interpolation but also retains the polynomial part of the bilinear system. The approach extends the interpolatory technique for index-1 linear DAEs [18] to bilinear DAEs. Numerical examples are used to illustrate the theoretical results.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Krylov-Subspace Based Model Reduction of Nonlinear Circuit Models Using Bilinear and Quadratic-Linear Approximations

We discuss Krylov-subspace based model reduction techniques for nonlinear control systems. Since reduction procedures of existent approaches like TPWL and POD methods are input dependent, models that are subject to variable excitations might not be sufficiently approximated. We will overcome this problem by generalizing Krylov-subspace methods known from linear systems to a more general class o...

متن کامل

Krylov subspace methods for model order reduction of bilinear control systems

We discuss the use of Krylov subspace methods with regard to the problem of model order reduction. The focus lies on bilinear control systems, a special class of nonlinear systems, which are closely related to linear systems. While most existent approaches are based on series expansions around zero, we will extend the underlying ideas to a more general context and show that there exist several ...

متن کامل

An Iterative Model Order Reduction Scheme for a Special Class of Bilinear Descriptor Systems Appearing in Constraint Circuit Simulation

We focus on interpolatory-based model order reduction for a special class of bilinear descriptor systems in the H2-optimal framework, appearing in constraint circuit simulations. The straightforward extension of the H2-optimality conditions for ODE systems to descriptor systems generically may produce an unbounded error in the H2 or H∞ norm, or both. This arises due to the inappropriate use of ...

متن کامل

Projection-Based Model Reduction for Time-Varying Descriptor Systems Using Recycled Krylov Subspaces

We will present a projection approach for model reduction of linear time-varying descriptor systems based on earlier ideas in the work of Philips and others. The idea behind the proposed procedure is based on a multipoint rational approximation of the monodromy matrix of the corresponding differential-algebraic equation. This is realized by orthogonal projection onto a rational Krylov subspace....

متن کامل

Model Reduction for Time-Varying Descriptor Systems Using Krylov-Subspaces Projection Techniques

We will present a projection approach for model reduction of linear time-varying descriptor systems based on earlier ideas in the work of Philips and others. The idea behind the proposed procedure is based on a multipoint rational approximation of the monodromy matrix of the corresponding differential-algebraic equation. This is realized by orthogonal projection onto a rational Krylov subspace....

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • J. Computational Applied Mathematics

دوره 315  شماره 

صفحات  -

تاریخ انتشار 2017