Energy-Based Approximation of Linear Systems with Polynomial Outputs

TL;DR

This paper introduces a polynomial-based energy approximation method for a class of nonlinear systems with linear states and polynomial outputs, extending linear model reduction techniques to nonlinear contexts.

Contribution

It presents a novel approach to approximate energy functions using low-rank tensors and generalizes balanced truncation for nonlinear systems with polynomial outputs.

Findings

Effective polynomial energy function approximation demonstrated

Low-rank tensor methods enable scalable computations

Accurate reduced models for nonlinear systems shown in experiments

Abstract

Controllability and observability energy functions play a fundamental role in model order reduction and are inherently connected to optimal control problems. For linear dynamical systems the energy functions are known to be quadratic polynomials and various low-rank approximation techniques allow for computing them in a large-scale setting. For nonlinear problems computing the energy functions is significantly more challenging. In this paper, we investigate a special class of nonlinear systems that have a linear state and a polynomial output equation. We show that the energy functions of these systems are again polynomials and investigate under which conditions they can effectively be approximated using low-rank tensors. Further, we introduce a new perspective on the well-established balanced truncation method for linear systems which then readily generalizes to the nonlinear systems under consideration. This new perspective yields a novel energy-based model order reduction procedure that accurately captures the input-output behavior of linear systems with polynomial outputs via a low-dimensional reduced order model. We demonstrate the effectiveness of our approach via two numerical experiments.