Optical phase estimation for a patch-type extrinsic Fabry–Perot interferometer sensor system and its application to flutter suppression

An optical phase tracking technique for an extrinsic Fabry–Perot interferometer (EFPI) is proposed in order to overcome interferometric non-linearity. The basic idea is utilizing strain rate information, which cannot be easily obtained from the EFPI sensor itself. The proposed phase tracking system consists of a patch-type EFPI sensor and a simple on-line phase tracking logic. The patch-type EFPI sensor comprises an EFPI and a piezoelectric patch. An EFPI sensor itself has non-linear behavior due to the interferometric characteristics, and a piezoelectric material has hysteresis. However, the composed patch-type EFPI sensor system overcomes the problems that can arise when they are used individually. The proposed system can extract vibration information from severely distorted EFPI sensor signals. The dynamic characteristics of the proposed phase tracking system were investigated, and then the patch-type EFPI sensor system was applied to the active suppression of flutter, dynamic aeroelastic instability, of a swept-back composite plate structure. The real time neural predictive control algorithm effectively reduces the amplitude of the flutter mode, and 6.5% flutter speed enhancement for the aeroelastic system was obtained by integrating smart materials into advanced structures.

[1]  Jae-Sung Bae,et al.  Linear and Nonlinear Aeroelastic Analysis of Fighter-Type Wing with Control Surface , 2002 .

[2]  Martin R. Waszak Modeling the Benchmark Active Control Technology Wind-Tunnel Model for Active Control Design Applications , 1998 .

[3]  Kent A. Murphy,et al.  EFPI manufacturing improvements for enhanced performance and reliability , 1995, Smart Structures.

[4]  J. S. Sirkis,et al.  Passive signal processing of in-line fiber etalon sensors for high strain-rate loading , 1997 .

[5]  In Lee,et al.  Application of Fiber Optic Sensor and Piezoelectric Actuator to Flutter Suppression , 2004 .

[6]  C. J. Harris,et al.  Neural Networks for Modelling and Control , 1994 .

[7]  In Lee,et al.  Flutter Suppression of a Lifting Surface Using Piezoelectric Actuation , 2000 .

[8]  Harley H. Cudney,et al.  A piezoelectric array for sensing vibration modal coordinates , 2001 .

[9]  Ratneshwar Jha,et al.  Neural-network-based adaptive predictive control for vibration suppression of smart structures , 2002 .

[10]  In Lee,et al.  Vibration control of structures with interferometric sensor non-linearity , 2004 .

[11]  In Lee,et al.  Optimal vibration control of a plate using optical fiber sensor and PZT actuator , 2003 .

[12]  Dong-Soo Kwon,et al.  Signal processing algorithm for transmission-type Fabry-Pérot interferometric optical fiber sensor , 2001 .

[13]  Kenneth B. Lazarus,et al.  Multivariable active lifting surface control using strain actuation : Analytical and experimental results , 1997 .

[14]  Shyan-Shu Shieh,et al.  Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks , 2003 .

[15]  Dongbing Gu,et al.  Neural predictive control for a car-like mobile robot , 2002, Robotics Auton. Syst..

[16]  Pam Haley,et al.  Generalized predictive control for active flutter suppression , 1997 .

[17]  Alfred Cuschieri,et al.  Thermal modelling of shape memory alloy fixator for medical application , 2002 .

[18]  Liviu Librescu,et al.  Flutter, Postflutter, and Control of a Supersonic Wing Section , 2002 .

[19]  R O Claus,et al.  Quadrature phase-shifted, extrinsic Fabry-Perot optical fiber sensors. , 1991, Optics letters.

[20]  Il-Bum Kwon,et al.  A digital signal processing algorithm for structural strain measurement by a 3 × 3 passive demodulated fiber optic interferometric sensor , 1999 .

[21]  S. Srinathkumar,et al.  Flutter suppression for the active flexible wing - A classical design , 1995 .

[22]  Jennifer Heeg,et al.  An analytical and experimental investigation of flutter suppression via piezoelectric actuation , 1992 .

[23]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[24]  Robert H. Scanlan,et al.  A Modern Course in Aeroelasticity , 1981, Solid Mechanics and Its Applications.