Paper
1 May 1996 Adaptive neural control of aeroelastic response
Peter F. Lichtenwalner, Gerald R. Little, Robert C. Scott
Author Affiliations +
Abstract
The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter F. Lichtenwalner, Gerald R. Little, and Robert C. Scott "Adaptive neural control of aeroelastic response", Proc. SPIE 2717, Smart Structures and Materials 1996: Smart Structures and Integrated Systems, (1 May 1996); https://doi.org/10.1117/12.239022
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Cited by 6 scholarly publications.
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KEYWORDS
Neural networks

Control systems

Adaptive control

Point spread functions

Smart structures

Systems modeling

Chlorine

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