Balanced truncation model reduction of periodic systems

The balanced truncation approach to model reduction is considered for linear discrete-time periodic systems with time-varying dimensions. Stability of the reduced model is proved and a guaranteed additive bound is derived for the approximation error. These results represent generalizations of the corresponding ones for standard discrete-time systems. Two numerically reliable methods to compute reduced order models using the balanced truncation approach are considered. The square-root method and the potentially more accurate balancing-free square-root method belong to the family of methods with guaranteed enhanced computational accuracy. The key numerical computation in both methods is the determination of the Cholesky factors of the periodic Gramian matrices by solving nonnegative periodic Lyapunov equations with time-varying dimensions directly for the Cholesky factors of the solutions

Generalizations of data-driven balancing: what to sample for different balancing-based reduced models

The Quadrature-based Balanced Truncation (QuadBT) framework of arXiv:2104.01006 is a "non-intrusive" reformulation of balanced truncation; a classical projection-based model-order reduction technique for linear systems. QuadBT is non-intrusive in the sense that it builds approximate balanced reduced-order models entirely from system response data (e.g., transfer function measurements) without the need to reference an explicit state-space realization of the underlying full-order model. In this work, we generalize and extend QuadBT to other types of balanced truncation model reduction. Namely, we develop non-intrusive implementations for balanced stochastic truncation, positive-real balanced truncation, and bounded-real balanced truncation. We show that the data-driven construction of these balanced reduced-order models requires sampling certain spectral factors associated with the system of interest. Numerical examples are included in each case to validate our approach.Comment: 13 pages, 3 figure

Empirical Model Reduction of Controlled Nonlinear Systems

In this paper we introduce a new method of model reduction for nonlinear systems with inputs and outputs. The method requires only standard matrix computations, and when applied to linear systems results in the usual balanced truncation. For nonlinear systems, the method makes used of the Karhunen-Lo`eve decomposition of the state-space, and is an extension of the method of empirical eigenfunctions used in fluid dynamics. We show that the new method is equivalent to balanced-truncation in the linear case, and perform an example reduction for a nonlinear mechanical system

Model Reduction for Linear Parameter-Dependent Systems

The paper considers the problem of model reduction for a class of linear parameter-dependent (LPD) systems. Three model reduction approaches: balanced truncation, balanced LQG truncation and gain-scheduled $mathcal{H}_infty$ model reduction, are presented to reduce the dimension of LPD systems. For the former two approaches, conditions to proceed the reduction are given in terms of a finite number of linear matrix inequalities (LMIs); while the latter one involves LMIs with some additional rank constraint

Modern CACSD using the Robust-Control Toolbox

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction

Balancing and model reduction for discrete-time nonlinear systems based on Hankel singular value analysis

This paper is concerned with balanced realization and model reduction for discrete-time nonlinear systems. Singular perturbation type balanced truncation method is proposed. In this procedure, the Hankel singular values and the related controllability and observability properties are preserved, which is a natural generalization of both the linear discrete-time case and the nonlinear continuous-time case.

Model Reduction by Balanced Truncation

Model reduction by balanced truncation for bounded real and positive real input-stateoutput systems, known as bounded real balanced truncation and positive real balanced truncation respectively, is addressed. Results for finite-dimensional systems were established in the mid to late 1980s and we consider two extensions of this work. Firstly, using a more behavioral framework we consider the notion of a finite-dimensional dissipative system, of which bounded real and positive real input-state-output systems are particular instances. Specifically, we work in a framework where we make no a priori distinction between inputs and outputs. We derive model reduction by dissipative balanced truncation, where a gap metric error bound is obtained, and demonstrate that the aforementioned bounded real and positive real balanced truncation can be seen as special cases. In the second part we generalise bounded real and positive real balanced truncation to classes of bounded real and positive real systems respectively that have non-rational transfer functions, so called infinite-dimensional systems. Here we work in the context of well-posed linear systems. We derive approximate transfer functions, which we prove are rational and preserve the relevant dissipativity property. We also obtain error bounds for the difference of the original transfer function and its reduced order transfer function, in the H-infinity norm and gap metric for the bounded real and positive real cases respectively. This extension to bounded real and positive real balanced truncation requires new results for Lyapunov balanced truncation in the infinite dimensional case, which we also describe. We conclude by highlighting possible future research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo