Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections
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Abstract (arXiv)
A central notion in systems theory is dissipativity, which has been introduced by Jan Willems with the explicit goal of arriving at a fundamental understanding of the stability properties of feedback interconnections. In robust control, the framework of integral quadratic constraints (IQCs) builds on the seminal contributions of Yakubovich and Zames in the 1960's. It provides a technique for analyzing the stability of an interconnection of some linear system in feedback with a whole class of systems, also refereed to as uncertainty. In this paper we survey the key ideas of exploiting dissipativity and integral quadratic constraints for the computational analysis of robust stability and performance properties of uncertain interconnections in terms of linear matrix inequalities. In particular for dynamic supply rates, the paper revolves around the notion of finite-horizon integral quadratic constraints with a terminal cost. We reveal that this provides a seamless link between the general IQC theorem and dissipativity theory that has been established only rather recently.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2021.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.
5 papers.
- 2024Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guaranteescited by 4
- 2024Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networkscited by 4
- 2023Local Lipschitz Constant Computation of ReLU-FNNs: Upper Bound Computation with Exactness Verificationcited by 2
- 2024A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizationscited by 2
- 2026Generalization as a robust performance property of learning-enabled dynamical systemscited by 0
- 2026Generalization as a robust performance property of learning-enabled dynamical systems31 Aug 2026
- 2024A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizations9 Jul 2024
- 2024Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks8 May 2024
- 2024Synthesizing Neural Network Controllers with Closed-Loop Dissipativity Guarantees10 Apr 2024
- 2023Local Lipschitz Constant Computation of ReLU-FNNs: Upper Bound Computation with Exactness Verification17 Oct 2023