Towards Robustness Guarantees for Feedback-Based Optimization

Conference · Thu Mar 12 04:00:00 EDT 2020

DOI:https://doi.org/10.1109/CDC40024.2019.9029953· OSTI ID:1669441

Colombino, Marcello; Simpson-Porco, John W.; Bernstein, Andrey

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Electricity, Advanced Grid Modeling Program
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1669441
• Report Number(s): NREL/CP-5D00-77151; MainId:26097; UUID:ac35e1ee-e714-438e-97bb-d33864a35f40; MainAdminID:13670
• Country of Publication: United States
• Language: English

Related Subjects

Jacobian matrices
POWER TRANSMISSION AND DISTRIBUTION
approximation algorithms
feedforward systems
optimization
predictive models
real-time systems
robustness