Data‐enabled extremum seeking: A cooperative concurrent learning‐based approach. (23rd October 2020)

• Record Type: Journal Article
• Authors: Poveda, Jorge I.; Benosman, Mouhacine; Vamvoudakis, Kyriakos G.
• Other Names: Benosman Mouhacine guestEditor.; Krstić Miroslav guestEditor.; Guay Martin guestEditor.; Teel Andrew guestEditor.
• Is Part Of: International journal of adaptive control and signal processing. Volume 35:Number 7(2021)
• Page Start: 1256
• Page End: 1284
• Publication Date: 2020-10-23
• Subjects: concurrent learning -- data‐driven optimization -- extremum seeking -- multi‐agent systems; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3189
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This paper introduces a new class of feedback‐based data‐driven extremum seeking algorithms for the solution of model‐free optimization problems in smooth continuous‐time dynamical systems. The novelty of the algorithms lies on the incorporation of memory to store recorded data that enables the use of information‐rich datasets during the optimization process, and allows to dispense with the time‐varying dither excitation signal needed by standard extremum seeking algorithms that rely on a persistence of excitation (PE) condition. The model‐free optimization dynamics are developed for single‐agent systems, as well as for multi‐agent systems with communication graphs that allow agents to share their state information while preserving the privacy of their individual data. In both cases, sufficient richness conditions on the recorded data, as well as suitable optimization dynamics modeled by ordinary differential equations are characterized in order to guarantee convergence to a neighborhood of the solution of the extremum seeking problems. The performance of the algorithms is illustrated via different numerical examples in the context of source‐seeking problems in multivehicle systems.