What is it about?
Feedback is ultimately necessary to achieve high performance in flow control over a wide range of operating conditions. As flow dynamics are difficult to model analytically, a (nonlinear) neural network is used to identify the dynamics and Sampling Based Model Predictive Optimization, a relatively new method for nonlinear model predictive control is used for feedback control. Using micro-jets, flow separation control is successfully demonstrated under varying operating conditions.
Featured Image
Why is it important?
Nonlinear adaptive control had previously not been demonstrated for flow control. This work provides its first demonstration and provides a methodology that can be used in a variety of flow control problems.
Perspectives
Adaptive Sampling Based Model Predictive Control enables control of nonlinear systems without having to analytically develop nonlinear dynamic models or use intuition and experience to construct Lyapunov functions. This powerful technique has first been experimentally applied to flow control. The results of this paper show the power of this new feedback control methodology.
Dr. Emmanuel G Collins
Florida A&M University-Florida State University College of Engineering
Read the Original
This page is a summary of: A Nonlinear Adaptive Method for Microjet-Based Flow Separation Control, June 2014, American Institute of Aeronautics and Astronautics (AIAA),
DOI: 10.2514/6.2014-2366.
You can read the full text:
Contributors
The following have contributed to this page
