Integral Quadratic Constraints with Infinite-Dimensional Channels

TL;DR

This paper extends the Integral Quadratic Constraints (IQCs) framework to infinite-dimensional systems like PDEs and DDEs, enabling stability analysis of complex control systems with uncertainties.

Contribution

It introduces an IQC-based framework for infinite-dimensional systems, including PDEs and DDEs, with new stability conditions and testing methods using infinite-dimensional multipliers.

Findings

Developed a formulation of hard IQC-based stability conditions for infinite-dimensional systems.

Proposed a method to test IQC conditions using the PIE state-space representation.

Validated the approach with four example problems involving uncertainty and nonlinearity.

Abstract

Modern control theory provides us with a spectrum of methods for studying the interconnection of dynamic systems using input-output properties of the interconnected subsystems. Perhaps the most advanced framework for such input-output analysis is the use of Integral Quadratic Constraints (IQCs), which considers the interconnection of a nominal linear system with an unmodelled nonlinear or uncertain subsystem with known input-output properties. Although these methods are widely used for Ordinary Differential Equations (ODEs), there have been fewer attempts to extend IQCs to infinite-dimensional systems. In this paper, we present an IQC-based framework for Partial Differential Equations (PDEs) and Delay Differential Equations (DDEs). First, we introduce infinite-dimensional signal spaces, operators, and feedback interconnections. Next, in the main result, we propose a formulation of hard IQC-based input-output stability conditions, allowing for infinite-dimensional multipliers. We then show how to test hard IQC conditions with infinite-dimensional multipliers on a nominal linear PDE or DDE system via the Partial Integral Equation (PIE) state-space representation using a sufficient version of the Kalman-Yakubovich-Popov lemma (KYP). The results are then illustrated using four example problems with uncertainty and nonlinearity.