Feedback Optimization of Dynamical Systems in Time-Varying Environments: An Internal Model Principle Approach

TL;DR

This paper introduces a novel feedback optimization framework for dynamical systems in time-varying environments, leveraging an internal model principle to improve tracking and adaptability beyond traditional methods.

Contribution

It presents a new design methodology that integrates internal models with output-feedback stabilization to enhance performance in time-varying optimization problems.

Findings

Enables tracking of time-varying optimizers with internal model incorporation

Overcomes slow convergence and adaptability limitations of existing methods

Provides a systematic approach for feedback optimization in dynamic settings

Abstract

Feedback optimization has emerged as a promising approach for regulating dynamical systems to optimal steady states that are implicitly defined by underlying optimization problems. Despite their effectiveness, existing methods face two key limitations: (i) reliable performance is restricted to time-invariant or slowly varying settings, and (ii) convergence rates are limited by the need for the controller to operate orders of magnitude slower than the plant. These limitations can be traced back to the reliance of existing techniques on numerical optimization algorithms. In this paper, we propose a novel perspective on the design of feedback optimization algorithms, by framing these objectives as an output regulation problem. We place particular emphasis on time-varying optimization problems, and show that an algorithm can track time-varying optimizers if and only if it incorporates a model of the temporal variability inherent to the optimization - a requirement that we term the internal model principle of feedback optimization. Building on this insight, we introduce a new design methodology that couples output-feedback stabilization with a control component that drives the system toward the critical points of the optimization problem. This framework enables feedback optimization algorithms to overcome the classical limitations of slow tracking and poor adaptability to time variations.