当前位置: X-MOL 学术Int. J. Adapt. Control Signal Process. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Data-enabled extremum seeking: A cooperative concurrent learning-based approach
International Journal of Adaptive Control and Signal Processing ( IF 3.1 ) Pub Date : 2020-10-23 , DOI: 10.1002/acs.3189
Jorge I. Poveda 1 , Mouhacine Benosman 2 , Kyriakos G. Vamvoudakis 3
Affiliation  

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.

中文翻译:

数据支持的极值搜索:一种基于协作并发学习的方法

本文介绍了一类新的基于反馈的数据驱动极值搜索算法,用于解决光滑连续时间动态系统中的无模型优化问题。算法的新颖之处在于结合内存来存储记录的数据,这使得在优化过程中能够使用信息丰富的数据集,并允许免除标准极值搜索算法所需的时变抖动激励信号,这些算法依赖于持续激发 (PE) 条件。无模型优化动态是为单代理系统以及具有通信图的多代理系统开发的,该通信图允许代理共享其状态信息,同时保护其个人数据的隐私。在这两种情况下,记录数据的足够丰富性条件,以及由常微分方程建模的合适的优化动力学特征,以保证收敛到极值寻求问题的解的邻域。在多车辆系统中的寻源问题的背景下,通过不同的数值示例说明了算法的性能。
更新日期:2020-10-23
down
wechat
bug