Finite Horizon Robust Synthesis Using Integral Quadratic Constraints

TL;DR

This paper introduces a robust synthesis method for uncertain linear time-varying systems over finite horizons, utilizing Integral Quadratic Constraints to ensure worst-case performance minimization.

Contribution

It proposes an iterative algorithm combining LTV synthesis and IQC analysis to handle non-convex optimization in finite horizon robust control.

Findings

Algorithm guarantees non-increasing robust performance at each iteration

Effective in minimizing worst-case performance for uncertain LTV systems

Demonstrated success through numerical examples

Abstract

We present a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons. The uncertain system is described as an interconnection of a known LTV system and a perturbation. The input-output behavior of the perturbation is specified by time-domain Integral Quadratic Constraints (IQCs). The objective is to synthesize a controller to minimize the worst-case performance. This leads to a non-convex optimization. The proposed approach alternates between an LTV synthesis step and an IQC analysis step. Both induced $\mathcal{L}_2$ and terminal Euclidean norm penalties on output are considered for finite horizon performance. The proposed algorithm ensures that the robust performance is non-increasing at each iteration step. The effectiveness of this method is demonstrated using numerical examples.