Gradient-Based Stochastic Extremum Seeking for Multivariable Systems With Distinct Input Delays

• 成果类型: Article
• 署名作者: Silva, Paulo Cesar Souza; Pellanda, Paulo Cesar; Oliveira, Tiago Roux
• 署名单位: Universidade do Estado do Rio de Janeiro
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3673189
• 发表日期: 2026
• 关键词: stability; time

摘要

This article addresses the design and analysis of a multivariable gradient-based stochastic extremum-seeking control method for multi-input systems with arbitrary input delays. The approach accommodates systems with distinct time delays across input channels. It achieves local exponential stability of the closed-loop system, guaranteeing convergence to a small neighborhood around the extremum point. By incorporating phase compensation for dither signals and a novel predictor-feedback mechanism with averaging-based estimates of the unknown gradient and Hessian, the proposed method overcomes traditional challenges associated with arbitrary, distinct input delays. Unlike previous work on deterministic multiparameter extremum-seeking with distinct input delays, this stability analysis is achieved without using backstepping transformations, simplifying the predictor design and enabling a more straightforward implementation. Specifically, the direct application of Artstein's reduction approach yields delay- and system-dimension-independent convergence rates, thus enhancing its practical applicability. A numerical example and a source-seeking application illustrate the robust performance and advantages of the proposed delay-compensated stochastic extremum-seeking method.