Less conservative robustness analysis of linear parameter varying systems using integral quadratic constraints

Harald Pfifer, Peter Seiler

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


This paper considers the robustness of a feedback connection of a known linear parameter varying system and a perturbation. A sufficient condition is derived to bound the worst-case gain and ensure robust asymptotic stability. The input/output behavior of the perturbation is described by multiple integral quadratic constraints (IQCs). The analysis condition is formulated as a dissipation inequality. The standard approach requires a non-negative definite storage function and the use of ‘hard’ IQCs. The term ‘hard’ means that the IQCs can be specified as time-domain integral constraints that hold over all finite horizons. The main result demonstrates that the dissipation inequality condition can be formulated requiring neither a non-negative storage function nor hard IQCs. A key insight used to prove this result is that the multiple IQCs, when combined, contain hidden stored energy. This result can lead to less conservative robustness bounds. Two simple examples are presented to demonstrate this fact.

Original languageEnglish (US)
Pages (from-to)3580-3594
Number of pages15
JournalInternational Journal of Robust and Nonlinear Control
Issue number16
StatePublished - Nov 10 2016

Bibliographical note

Funding Information:
This work was partially supported by the AFOSR under the grant entitled A Merged IQC/SOS Theory for Analysis of Nonlinear Control Systems, Dr. Fahroo technical monitor. This work was also partially supported by the National Science Foundationunder Grant No. NSF-CMMI-1254129 entitled CAREER: Probabilistic Tools for High Reliability Monitoring and Control of Wind Farms.

Publisher Copyright:
Copyright © 2016 John Wiley & Sons, Ltd.


  • linear parameter varying systems
  • robust control
  • uncertain systems


Dive into the research topics of 'Less conservative robustness analysis of linear parameter varying systems using integral quadratic constraints'. Together they form a unique fingerprint.

Cite this