Towards Robustness Guarantees for Feedback-Based Optimization

Marcello Colombino, John Simpson-Porco, Andrey Bernstein

Research output: Contribution to conferencePaperpeer-review

25 Scopus Citations


Feedback-based online optimization algorithms have gained traction in recent years because of their simple implementation, their ability to reject disturbances in real time, and their increased robustness to model mismatch. While the robustness properties have been observed both in simulation and experimental results, the theoretical analysis in the literature is mostly limited to nominal conditions. In this work, we propose a framework to systematically assess the robust stability of feedback-based online optimization algorithms. We leverage tools from monotone operator theory, variational inequalities and classical robust control to obtain tractable numerical tests that guarantee robust convergence properties of online algorithms in feedback with a physical system, even in the presence of disturbances and model uncertainty. The results are illustrated via an academic example and a case study of a power distribution system.

Original languageAmerican English
Number of pages8
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: 11 Dec 201913 Dec 2019


Conference58th IEEE Conference on Decision and Control, CDC 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

NREL Publication Number

  • NREL/CP-5D00-77151


  • approximation algorithms
  • feedforward systems
  • Jacobian matrices
  • optimization
  • predictive models
  • real-time systems
  • robustness


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