arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1404.4589physics.flu-dynnlin.CD
收藏

使用机器学习的闭环Turbulence控制

Closed-Loop Turbulence Control Using Machine Learning

Thomas Duriez, Vladimir Parezanović, Laurent Cordier, Bernd R. Noack, Joël Delville, Jean-Paul Bonnet, Marc Segond, Markus Abel

摘要:

我们提出了一个通用的无模型策略的闭环控制设计的湍流。这种被称为“机器学习控制”(MLC)的策略能够以系统的无监督方式利用非线性机制。它依赖于一个进化算法,该算法用于进化一系列闭环控制法则,直到目标成本函数最小化。该方法可应用于任何非线性多输入多输出(MIMO)系统,以获得最优闭环控制律。MLC被成功地应用于非线性耦合振荡器的稳定表现出频率串扰,最大化的最大李雅普诺夫指数的强迫洛伦兹系统,并在实验混合层流的混合增强。我们预见到许多潜在的应用,最非线性MIMO控制问题,特别是在实验中。

英文摘要:

We propose a general model-free strategy for feedback control design of turbulent flows. This strategy called 'machine learning control' (MLC) is capable of exploiting nonlinear mechanisms in a systematic unsupervised manner. It relies on an evolutionary algorithm that is used to evolve an ensemble of feedback control laws until minimization of a targeted cost function. This methodology can be applied to any non-linear multiple-input multiple-output (MIMO) system to derive an optimal closed-loop control law. MLC is successfully applied to the stabilization of nonlinearly coupled oscillators exhibiting frequency cross-talk, to the maximization of the largest Lyapunov exponent of a forced Lorenz system, and to the mixing enhancement in an experimental mixing layer flow. We foresee numerous potential applications to most nonlinear MIMO control problems, particularly in experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑