论文标题
主动流控制机器学习方法的比较分析
Comparative analysis of machine learning methods for active flow control
论文作者
论文摘要
诸如遗传编程（GP）和增强学习（RL）之类的机器学习框架在流量控制方面越来越受欢迎。这项工作对两者进行了比较分析，标明了其一些最具代表性的算法，以针对贝叶斯优化（BO）和Lipschitz全球优化（LIPO）等全球优化技术。首先，我们回顾了无模型控制问题的一般框架，将所有方法汇总为黑框优化问题。然后，我们在三个测试用例上测试控制算法。这些是（1）具有频率串扰的非线性动力学系统的稳定，（2）从汉堡的流量取消波浪和（3）圆柱尾流的阻力减小。我们提出了一个全面的比较，以说明它们在探索与剥削方面的差异及其在控制法定义中的“模型能力”之间的平衡与“必需的复杂性”之间的平衡。我们认为，这种比较为各种方法的杂交铺平了道路，我们为他们在流动控制问题的文献中提供了一些观点。
Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques such as Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between `model capacity' in the control law definition versus `required complexity'. We believe that such a comparison paves the way toward the hybridization of the various methods, and we offer some perspective on their future development in the literature on flow control problems.
