使用机器学习的闭环分离控制
Closed-loop separation control using machine learning
摘要：
一种新的，无模型的，实验闭环流量控制的方法是在分离流上实现的。使用遗传编程生成闭环控制律，其中使用最佳执行律的复制、突变和交叉来优化它们以产生新一代候选控制律。这个优化过程自动应用到一个后向台阶流在Re=1350，由一个缝射流控制，产生一个有效的控制律。提出了收敛准则。该法律是能够产生有效的行动，即使在流动状态的重大变化，证明其鲁棒性。基本的物理机制杠杆的法律进行了分析和讨论。与传统的剪切层周期性强迫相反，这种新的控制律对台阶下游回流区的物理特性起作用。虽然这两种控制措施有着根本的不同，但它们仍然达到了相同的有效性水平。此外，新的法律也可能更容易和更便宜地实现致动器明智。
英文摘要：
A novel, model free, approach to experimental closed-loop flow control is implemented on a separated flow. Feedback control laws are generated using genetic programming where they are optimized using replication, mutation and cross-over of best performing laws to produce a new generation of candidate control laws. This optimization process is applied automatically to a backward-facing step flow at Re=1350, controlled by a slotted jet, yielding an effective control law. Convergence criterion are suggested. The law is able to produce effective action even with major changes in the flow state, demonstrating its robustness. The underlying physical mechanisms leveraged by the law are analyzed and discussed. Contrary to traditional periodic forcing of the shear layer, this new control law plays on the physics of the recirculation area downstream the step. While both control actions are fundamentally different they still achieve the same level of effectiveness. Furthermore the new law is also potentially easier and cheaper to implement actuator wise.
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。
