Energy function approximations for differential algebraic polynomial systems of Stokes-type

TL;DR

This paper develops polynomial approximation methods for energy functions in nonlinear systems with Stokes-type DAE structures, enabling efficient control analysis and preserving system sparsity.

Contribution

It extends Kronecker product-based polynomial approximations to Stokes-type DAEs, maintaining sparsity and applying to feedback control problems.

Findings

Polynomial approximations effectively solve HJB equations for Stokes-type DAEs.

The approach preserves the original sparsity of the system.

Demonstrated on two polynomial feedback control problems.

Abstract

Energy functions are generalizations of controllability and observability Gramians to nonlinear systems and as such find applications in both nonlinear balanced truncation and feedback control. These energy functions are solutions to the Hamilton-Jacobi-Bellman (HJB) equations partial differential equations defined over spatial dimensions determined by the number of state variables in the nonlinear system. Thus, they cannot be resolved with local basis functions for problems of even modest dimension. In this paper, we extend recent results that utilize Kronecker products to generate polynomial approximations to HJB equations. Specifically, we consider the addition of linear drift terms that exhibit a Stokes-type differential-algebraic equation (DAE) structure. This extension leverages the so-called strangeness framework for DAEs to create separate sets of algebraic and differential variables with differential equations that only involve the differential variables and algebraic equations that relate them both. At this point, the existing polynomial approximations using Kronecker products can be applied to find approximations to the energy functions. This approach is demonstrated for two polynomial feedback control problems. The standard transformation destroys the sparsity in the original system. Thus, we also present a formulation that preserves the original sparsity structure.