Hybrid Set-Seeking Systems: Model-Free Feedback Optimization via Hybrid Inclusions

TL;DR

This paper introduces hybrid extremum-seeking systems, combining continuous and discrete dynamics, and provides control-theoretic analysis, design methods, and practical examples for model-free optimization in hybrid and cyber-physical systems.

Contribution

It offers a comprehensive tutorial on hybrid extremum-seeking control, extending perturbation analysis to hybrid systems with new design and evaluation techniques.

Findings

Hybrid extremum-seeking algorithms are effective for static and dynamic plants.

Perturbation methods extend naturally to hybrid systems under mild assumptions.

Examples include switching, obstacle-avoidance, and safety-constrained extremum seeking.

Abstract

This article aims to provide an accessible, tutorial-style introduction to hybrid extremum-seeking systems, which are model-free, feedback-optimization controllers that incorporate hybrid dynamics, meaning both continuous-time and discrete-time behaviors. Such systems arise when advanced control and optimization tools are needed to overcome the limitations of smooth feedback methods and to satisfy demanding transient and steady-state requirements in high-performance applications. They also appear when controllers must operate on plants that inherently exhibit hybrid behaviors, as is common in cyber-physical and autonomous systems that rely on digital sensing, computation, and actuation. To study hybrid extremum-seeking dynamics through control-theoretic methods, we first review the key concepts that support the development of perturbation theory for hybrid inclusions, forming the basis for averaging and singular perturbation analyses. We then show how these ideas apply to the design and evaluation of hybrid extremum-seeking algorithms for static and dynamic plants. Several examples are presented, including set-valued and switching algorithms under different switching regimes such as arbitrarily fast switching, dwell-time and average dwell-time constraints, and average activation time conditions. We also discuss state-based switching extremum seeking for obstacle-avoidance problems and gradient-Newton switching schemes. Additional topics include momentum-based and reset-type extremum seeking, intermittent updates, slowly varying parameters, hybrid filters, and safety-aware schemes that incorporate constraints. Across all these settings, we illustrate how perturbation-based methods traditionally used for extremum-seeking control naturally extend to hybrid systems when mild regularity assumptions are satisfied, and solutions are modeled on hybrid time domains.