Robust Adaptive Extremum Seeking Control Without Persistence of Excitation: Theory to Experiment
In this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the...
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Veröffentlicht in: | International journal of robust and nonlinear control 2024-11 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | In this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios. |
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ISSN: | 1049-8923 1099-1239 |
DOI: | 10.1002/rnc.7707 |