Integration of Flight Control and Flow Control

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$100,000.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-05-M-3539
Agency Tracking Number:
F051-243-1187
Solicitation Year:
2005
Solicitation Topic Code:
AF05-243
Solicitation Number:
2005.1
Small Business Information
INTELLIGENT AUTOMATION, INC.
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
Y
Duns:
161911532
Principal Investigator:
Xiaodong Zhang
Senior Research Engineer
(301) 294-5269
xzhang@i-a-i.com
Business Contact:
Mark James
Contracts & Proposals Manager
(301) 294-5221
mjames@i-a-i.com
Research Institution:
n/a
Abstract
Past research work has shown the great potentials of active flow control by using collectively interacting synthetic jet arrays, where the actuation signals to the jet actuators were generated by open-loop strategies. In this proposal, Intelligent Automation, Inc. (IAI) and its subcontractor, Prof. Amitay of Rensselaer Polytechnic Institute, propose a novel approach to integrated flight control and flow control to improve the aerodynamic performance of air vehicles. The proposed approach consists of two main components. First, the degree of flow separation is controlled by using synthetic jet arrays operating at high frequencies, resulting in the formation of a quasi-steady closed recirculating flow region. Additionally, synthetic jet actuators have some unique properties including zero-mass-flux, low power consumption, and compact structure well suited for MEMS applications, etc. Second, a neural network based nonlinear adaptive control method (NAC) is designed to regulate the actuation signals to jet actuators to track a reference trajectory of the desired degree of flow reattachment. A major advantage of the proposed adaptive control scheme is its minimal dependence on an accurate model of the complicated system dynamics. In addition, a pseudo-control hedging method is used to overcome neural network adaptation difficulties due to various actuation anomalies.

* information listed above is at the time of submission.

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