$\mathcal H_2$-Quasi-Optimal Model Order Reduction for Quadratic-Bilinear Control Systems

TL;DR

This paper develops a new $\\mathcal{H}_2$-quasi-optimal model reduction method for large-scale quadratic-bilinear control systems, introducing a variational analysis, a truncated norm, and an iterative algorithm with efficient Hessian computation.

Contribution

It presents a novel $\\mathcal{H}_2$-quasi-optimal reduction framework for QB systems, including new optimality conditions and an efficient iterative reduction algorithm.

Findings

The method effectively reduces system order while maintaining accuracy.

Numerical examples demonstrate competitiveness with existing schemes.

The approach efficiently computes the reduced Hessian using Kronecker structures.

Abstract

We investigate the optimal model reduction problem for large-scale quadratic-bilinear (QB) control systems. Our contributions are threefold. First, we discuss the variational analysis and the Volterra series formulation for QB systems. We then define the $\mathcal H_2$-norm for a QB system based on the kernels of the underlying Volterra series and also propose a truncated $\mathcal H_2$-norm. Next, we derive first-order necessary conditions for an optimal approximation, where optimality is measured in term of the truncated $\mathcal H_2$-norm of the error system. We then propose an iterative model reduction algorithm, which upon convergence yields a reduced-order system that approximately satisfies the newly derived optimality conditions. We also discuss an efficient computation of the reduced Hessian, using the special Kronecker structure of the Hessian of the system. We illustrate the efficiency of the proposed method by means of several numerical examples resulting from semi-discretized nonlinear partial differential equations and show its competitiveness with the existing model reduction schemes for QB systems such as moment-matching methods and balanced truncation.