Balanced Truncation Model Order Reduction For Quadratic-Bilinear Control Systems

TL;DR

This paper introduces a novel algebraic approach to balanced truncation for large-scale quadratic-bilinear systems, enabling efficient model order reduction by leveraging Gramians derived from Volterra series and Lyapunov equations.

Contribution

It proposes algebraic Gramians for QB systems based on Volterra series, facilitating model reduction and stability analysis, which overcomes computational challenges of traditional nonlinear Gramians.

Findings

Efficient model reduction demonstrated on nonlinear PDEs

Proposed Gramians relate to quadratic Lyapunov equations

Reduced systems maintain stability and accuracy

Abstract

We discuss balanced truncation model order reduction for large-scale quadratic-bilinear (QB) systems. Balanced truncation for linear systems mainly involves the computation of the Gramians of the system, namely reachability and observability Gramians. These Gramians are extended to a general nonlinear setting in Scherpen (1993), where it is shown that Gramians for nonlinear systems are the solutions of state-dependent nonlinear Hamilton-Jacobi equations. Therefore, they are not only difficult to compute for large-scale systems but also hard to utilize in the model reduction framework. In this paper, we propose algebraic Gramians for QB systems based on the underlying Volterra series representation of QB systems and their Hilbert adjoint systems. We then show their relations with a certain type of generalized quadratic Lyapunov equation. Furthermore, we present how these algebraic Gramians and energy functionals relate to each other. Moreover, we characterize the reachability and observability of QB systems based on the proposed algebraic Gramians. This allows us to find those states that are hard to control and hard to observe via an appropriate transformation based on the Gramians. Truncating such states yields reduced-order systems. Additionally, we present a truncated version of the Gramians for QB systems and discuss their advantages in the model reduction framework. We also investigate the Lyapunov stability of the reduced-order systems. We finally illustrate the efficiency of the proposed balancing-type model reduction for QB systems by means of various semi-discretized nonlinear partial differential equations and show its competitiveness with the existing moment-matching methods for QB systems.