Neural predictive control for active buffet alleviation

Lawrence E. Pado; Peter F. Lichtenwalner; Salvatore L. Liguore; Donald Drouin

doi:10.1117/12.310672

16 June 1998 Neural predictive control for active buffet alleviation

Lawrence E. Pado, Peter F. Lichtenwalner, Salvatore L. Liguore, Donald Drouin

Proceedings Volume 3326, Smart Structures and Materials 1998: Industrial and Commercial Applications of Smart Structures Technologies; (1998) https://doi.org/10.1117/12.310672
Event: 5th Annual International Symposium on Smart Structures and Materials, 1998, San Diego, CA, United States

Abstract

The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

Citation Download Citation

Lawrence E. Pado, Peter F. Lichtenwalner, Salvatore L. Liguore, and Donald Drouin "Neural predictive control for active buffet alleviation", Proc. SPIE 3326, Smart Structures and Materials 1998: Industrial and Commercial Applications of Smart Structures Technologies, (16 June 1998); https://doi.org/10.1117/12.310672

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