Removing Time-Scale Separation in Feedback-Based Optimization via Estimators

TL;DR

This paper introduces an estimator-based feedback optimization method that removes the need for slow controller dynamics, enabling faster convergence by utilizing plant model information, demonstrated in power system frequency control.

Contribution

It proposes a novel estimator-based modification of feedback-based optimization that eliminates the traditional time-scale separation constraint, improving convergence speed.

Findings

Eliminates the need for slow controller timescales in feedback optimization.

Achieves convergence rate limited only by the system's dominant eigenvalue.

Demonstrates effectiveness in power system frequency control with inverter resources.

Abstract

Feedback-based optimization (FBO) provides a simple control framework for regulating a stable dynamical system to the solution of a constrained optimization problem in the presence of exogenous disturbances, and does so without full knowledge of the plant dynamics. However, closed-loop stability requires the controller to operate on a sufficiently slower timescale than the plant, significantly constraining achievable closed-loop performance. Motivated by this trade-off, we propose an estimator-based modification of FBO which leverages dynamic plant model information to eliminate the time-scale separation requirement of traditional FBO. Under this design, the convergence rate of the closed-loop system is limited only by the dominant eigenvalue of the open-loop system. We extend the approach to the case of design based on only an approximate plant model when the original system is singularly perturbed. The results are illustrated via an application to fast power system frequency control using inverter-based resources.