A Novel Approach to Adaptive Flow Separation Control
Abstract
Due to their practical import, flow control problems have attracted increasing attention. This research specifically considers flow separation control, which can provide greater maneuverability and performance for the controlled system as well as reduced vibration. In particular, it considers control of flow separation over a NACA-0025 airfoil using microjet actuators and develops Adaptive Sampling Based Model Predictive Control (Adaptive SBMPC), a novel approach to Nonlinear Model Predictive Control that applies the Minimal Resource Allocation Network algorithm for nonlinear system identification and the Sampling Based Model Predictive Optimization (SBMPO) algorithm to achieve effective nonlinear control. Through pressure data and flow characterization from wind tunnel experiments, effective and robust separation control is demonstrated and it is seen that the methods computational efficiency is sufficient for successful real time implementation. Furthermore, this research shows that SBMPC is guaranteed to find the global minimum subject to the sampling if the prediction horizon is sufficiently long. On problems of increasingly complexity it is demonstrated to avoid the local minima to which gradient-based methods tend to converge and is also shown to be effective with a multi-input, multi-output, time-varying power system combustion control problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 03, 2016
- Accession Number
- AD1028475
Entities
People
- Emmanuel G. Collins
Organizations
- Florida A&M University