Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T23:47:20.950Z Has data issue: false hasContentIssue false

New adaptive controller method for SMA hysteresis modelling of a morphing wing

Published online by Cambridge University Press:  03 February 2016

T. L. Grigorie
Affiliation:
Laboratory of Research in Active Controls, Avionics and AeroServoElasticity LARCASE, École de Technologie Supérieure, www.larcase.etsmtl.ca, Montréal, Quebec, Canada
R. M. Botez
Affiliation:
Laboratory of Research in Active Controls, Avionics and AeroServoElasticity LARCASE, École de Technologie Supérieure, www.larcase.etsmtl.ca, Montréal, Quebec, Canada

Abstract

A neuro-fuzzy controller method for smart material actuator (SMA) hysteresis modelling is presented, conceived for a morphing wing application. The controller correlates each set of forces and electrical currents that are applied to the smart material actuators with the actuator elongation. The actuator is experimentally tested for four forces, using a variable electrical current. The final controller is obtained through the Matlab/Simulink integration of three independent neuro-fuzzy controllers, designed for the increase and decrease of electrical current, and for null electrical current in the cooling phase of the actuator. This final controller gives a very small error with respect to the experimental values.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2010 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Rodriguez, A.R., Morphing aircraft technology survey, 2007, Paper AIAA-2007-1258, 45th AIAA Aerospace Sciences Meeting and Exhibition, 8-11 January 2007, Reno, Nevada.Google Scholar
2. Moorhouse, D., Sanders, B., Von Spakovsky, M. and Butt, J., Benefits and design challenges of adaptive structures for morphing aircraft, Aeronaut J, 2006, 110, (1105), pp 157162.Google Scholar
3. Popov, A.V., Botez, R. and Labib, M., Transition point detection from the surface pressure distribution for controller design. J Aircr, 2008, 45, (1), pp 2328.Google Scholar
4. Song, G., Chaudhry, V. and Batur, C., A neural network inverse model for a shape memory alloy wire actuator. J Intelligent material Systems and Structures, 2003, 14, (6), pp 371377.Google Scholar
5. Lee, H.J., Lee, J.J., Kwon, D.S. and Yoon, Y.S., Neural network based control of SMA actuator for the active catheter, J HWRS-ERC, 2001, pp 16.Google Scholar
6. Sivanandam, S.N., Sumathi, S. and Deepa, S.N., Introduction to Fuzzy Logic using MATLAB, 2007, Springer, Berlin Heidelberg.Google Scholar
7. Kosko, B., Neural Networks and Fuzzy Systems — A Dynamical Systems Approach to Machine Intelligence, 1992, Prentice Hall, New Jersey, USA.Google Scholar
8. Matlab fuzzy logic and neural network toolboxes — Help.Google Scholar
9. Mahfoud, M., Linkens, D.A. and Kandiah, S., Fuzzy Takagi-Sugeno Kang model predictive control for process engineering, 1999, 4 pp, IEE, London, UK.Google Scholar
10. Kung, C.C. and Su, J.Y., Affine Takagi-Sugeno fuzzy modeling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion, IET Control Theory and Applications, 2007, 1, (5), pp 12551265.Google Scholar