Novel Conditions for the Finite-Region Stability of 2D-Systems with Application to Iterative Learning Control

TL;DR

This paper introduces less conservative conditions for finite-region stability of 2D linear systems, applies the theory to iterative learning control to ensure finite-time error convergence, and provides LMI-based solutions validated by numerical examples.

Contribution

It presents new sufficient conditions for finite-region stability of 2D systems and applies them to improve iterative learning control performance.

Findings

Less conservative FRS conditions for 2D systems

Finite-time convergence of ILC tracking errors

LMI-based optimization framework for stability analysis

Abstract

Some recent papers have extended the concept of finite-time stability (FTS) to the context of 2D linear systems, where it has been referred to as finite-region stability (FRS). FRS methodologies make even more sense than the classical FTS approach developed for 1D-systems, since, typically, at least one of the state variables of 2D-systems is a space coordinate, rather than a time variable. Since space coordinates clearly belong to finite intervals, FRS techniques are much more effective than the classical Lyapunov approach, which looks to the asymptotic behavior of the system over an infinite interval. To this regard, the novel contribution of this paper goes in several directions. First, we provide a novel sufficient condition for the FRS of linear time-varying (LTV) discrete-time 2D-systems, which turns out to be less conservative than those ones provided in the existing literature. Then, an interesting application of FRS to the context of iterative learning control (ILC) is investigated, by exploiting the previously developed theory. In particular, a new procedure is proposed so that the tracking errors of the ILC law converges within the desired bound in a finite number of iterations. Finally, a sufficient condition to solve the finite-region stabilization problem is proposed. All the results provided in the paper lead to optimization problems constrained by linear matrix inequalities (LMIs), that can be solved via widely available software. Numerical examples illustrate and validate the effectiveness of the proposed technique.