Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-18T21:21:56.468Z Has data issue: false hasContentIssue false

A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy Part 1: Morphing system mechanisms and controller architecture design

Published online by Cambridge University Press:  27 January 2016

T. L. Grigorie
Affiliation:
École de Technologie Supérieure, Montréal, Québec, Canada
R. M. Botez
Affiliation:
École de Technologie Supérieure, Montréal, Québec, Canada
A. V. Popov
Affiliation:
École de Technologie Supérieure, Montréal, Québec, Canada
M. Mamou
Affiliation:
National Research Council, Ottawa, Ontario, Canada
Y. Mébarki
Affiliation:
National Research Council, Ottawa, Ontario, Canada

Abstract

The present paper describes the design of a hybrid actuation control concept, a fuzzy logic proportional-integral-derivative plus a conventional on-off controller, for a new morphing mechanism using smart materials as actuators, which were made from shape memory alloys (SMA). The research work described here was developed for the open loop phase of a morphing wing system, whose primary goal was to reduce the wing drag by delaying the transition (from laminar to fully turbulent flows) position toward the wing trailing edge. The designed controller drives the actuation system equipped with SMA actuators to modify the flexible upper wing skin surface. The designed controller was also included, as an internal loop, in the closed loop architecture of the morphing wing system, based on the pressure information received from the flexible skin mounted pressure sensors and on the estimation of the transition location.

The controller’s purposes were established following a comprehensive presentation of the morphing wing system architecture and requirements. The strong nonlinearities of the SMA actuators’ characteristics and the system requirements led to the choice of a hybrid controller architecture as a combination of a bi-positional on-off controller and a fuzzy logic controller (FLC). In the chosen architecture, the controller would behave as a switch between the SMA cooling and heating phases, situations where the output current is 0A or is controlled by the FLC.

In the design phase, a proportional-integral-derivative scheme was chosen for the FLC. The input-output mapping of the fuzzy model was designed, taking account of the system’s error and its change in error, and a final architecture for the hybrid controller was obtained. The shapes chosen for the inputs’ membership functions were s -function, π-function, and z -function, and product fuzzy inference and the center average defuzzifier were applied (Sugeno).

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2012 

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. Patel, S.C., Majji, M., Koh, B.S., Junkins, J.L. and Rediniotis, O.K. Morphing wing: A demonstration of aero servo elastic distributed sensing and control, 2005, Final research paper in 2005 Summer Research Experience for Undergraduates (REU) on Nanotechnology and Materials Systems, Texas Institute of Intelligent Bio-Nano Materials and Structures for Aerospace Vehicles (TiiMS) – NASA Research University, Texas A&M University, July 2005, College Station, TX, USA.Google Scholar
2. Gano, S.E and Renaud, J.E. Optimized unmanned aerial vehicle with wing morphing for extended range and endurance, 2002, Ninth AIAA/ISSMO Symposium and Exhibition on Multidisciplinary Analysis and Optimization, 4-6 September 2002, Atlanta, GA, USA, pp 19.Google Scholar
3. Cadogan, D., Smith, T., Uhelsky, F. and Mackusick, M. Morphing inflatable wing development for compact package unmanned aerial vehicles, 2004, 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 19-22 April 2004, Palm Springs, CA, USA.Google Scholar
4. Popov, A.-V., Botez, R.M., Mamou, M., Mebarki, Y., Jahrhaus, B., Khalid, M. and Grigorie, T.L. Drag reduction by improving laminar flows past morphing configurations, 2009, AVT-168 NATO Symposium on the Morphing Vehicles, 20-23 April 2009, Evora, Portugal.Google Scholar
5. Wlezien, R.W., Horner, G.C., McGowan, A.R., Padula, S.L., Scott, M.A., Silcox, R.J. and Simpson, J.O. The aircraft morphing program, AIAA-1998-1927.Google Scholar
6. Bliss, T.K. and Bart-Smith, H. Morphing structures technology and its application to flight control, 2005, Student Research Conference, Virginia Space Grant Consortium, 1 April 2005, Newport News, VA, USA.Google Scholar
7. Whitmer, C.E and Kelkar, A.G. Robust control of a morphing airfoil structure, 2005, American Control Conference, 8-10 June 2005, Portland, OR, USA.Google Scholar
8. Ruotsalainen, P., Nevala, P.K., Brander, T., Lindroos, T. and Sippola, M. Shape control of a FRP airfoil structure using SMA-actuators and optical fiber sensors, J Solid State Phenomena, 2009, 144, Mechatronic Systems and Materials II, pp 196201.Google Scholar
9. Lampton, A., Niksch, A. and Valasek, J. Reinforcement learning of a morphing airfoil-policy and discrete learning analysis, 2008, AIAA Guidance, Navigation and Control Conference and Exhibition, 18-21 August 2008, Honolulu, Hawaii, USA.Google Scholar
10. Lampton, A., Niksch, A. and Valasek, J. Morphing airfoils with four morphing parameters, 2008, AIAA Guidance, Navigation and Control Conference and Exhibition, 18-21 August 2008, Honolulu, Hawaii, USA.Google Scholar
11. Al-Odienat, A.I. and Al-Lawama, A.A. The advantages of PID fuzzy controllers over the conventional types, American J Applied Sciences, 2008, 5, (6), pp 653658.Google Scholar
12. Kovacic, Z. and Bogdan, S. Fuzzy Controller Design – Theory and Applications, 2006, Taylor and Francis Group.Google Scholar
13. Verbruggen, H.B. and Bruijn, P.M. Fuzzy control and conventional control: What is (and can be) the real contribution of fuzzy systems?, Fuzzy Sets Systems, September 1997, 90, (2), pp 151160.Google Scholar
14. Hampel, R., Wagenknecht, M. and Chaker, N. Fuzzy control – Theory and practice, Physica-Verlag, 2000.Google Scholar
15. Zadeh, L.A. Fuzzy sets, Information Control, 1965, 8, pp 339353.Google Scholar
16. Luo, J. and Lan, E. Fuzzy Logic and Intelligent Systems – Fuzzy Logic Controllers for Aircraft Flight Control, 7 July 2007, pp 85124, Springer.Google Scholar
17. Vick, A. and Cohen, K. Longitudinal stability augmentation using a fuzzy logic based PID controller, 2009, Fuzzy Information Processing Society, 2009, NAFIPS 2009, Annual Meeting of the North American, 14-17 June 2009, pp 1-6.Google Scholar
18. Kurnaz, S., Cetin, O. and Kaynak, O. Fuzzy logic based approach to design of flight control and navigation tasks for autonomous unmanned aerial vehicles, J Intelligent and Robotic Systems, 2009, 54, pp 229244 Google Scholar
19. Ursu, I. and Ursu, F. An intelligent ABS control based on fuzzy logic. Aircraft application, 2003, Proceedings of the International Conference on Theory and Applications of Mathematics and Informatics – ICTAMI, 2003, Alba Iulia, Romania, pp 355368.Google Scholar
20. Ursu, I. and Ursu, F. Airplane ABS control synthesis using fuzzy logic, J Intelligent & Fuzzy Systems: Applications in Engineering and Technology, January 2005, 16, (1), pp 2332.Google Scholar
21. Stewart, P., Gladwin, D., Parr, M. and Stewart, J. Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft, 2010, Proceedings of the Institution of Mechanical Engineers, Part G, J Aerospace Engineering, 1 March 2010, 224, (3), pp 293309.Google Scholar
22. Hossain, A., Rahman, A., Hossen, J., Iqbal, A.K.M.P. and Hasan, S.K. Application of fuzzy logic approach for an aircraft model with and without winglet, Int J Mechanical, Industrial and Aerospace Eng, 2010, 4, (2), pp 7886.Google Scholar
23. Hiliuta, A., Botez, R.M. and Brenner, M. Approximation of unsteady aerodynamic forces Q, k, M by use of fuzzy techniques. AIAA J, 2005, 43, (10), pp 20932099.Google Scholar
24. Kouba, G., Botez, R.M. and Boely, N. Identification of F/A-18 model from flight tests using the fuzzy logic method, 2009, 47th AIAA Aerospace Sciences Meeting, 5-8 January 2009, Orlando, FL, USA.Google Scholar
25. Boely, N., Botez, R.M. and Kouba, G. Identification of an F/A-18 nonlinear model between control and structural deflections, 2009, 47th AIAA Aerospace Sciences Meeting, 5–8 January 2009, Orlando, FL, USA.Google Scholar
26. Grigorie, L.T. and Botez, R.M. New adaptive controller method for SMA hysteresis modelling of a morphing wing, Aeronaut J, January 2010, 114, (1151), pp 113.Google Scholar
27. Grigorie, L.T. and Botez, R.M. Adaptive neuro-fuzzy inference system based controllers for smart material actuator modeling, J Aerospace Eng, 2009, 223, (G6), pp 655668.Google Scholar
28. Popov, A-V., Botez, R.M., Mamou, M. and Grigorie, T.L. Optical sensor pressure measurements variations with temperature in wind tunnel testing, AIAA J Aircr, 2009, 46, (4), pp 13141318.Google Scholar
29. Popov, A-V., Labib, M., Fays, J. and Botez, R.M. Closed loop control simulations on a morphing laminar airfoil using shape memory alloys actuators, AIAA J Aircr, 2008, 45, (5), pp 17941803.Google Scholar
30. Sainmont, C., Paraschivoiu, I. and Coutu, D. Multidisciplinary approach for the optimization of a laminar airfoil equipped with a morphing upper surface, 2009, NATO AVT-168 Symposium on ‘Morphing Vehicule’, Evora, Portugal.Google Scholar
31. Georges, T., Brailovski, V., Morellon, E., Coutu, D. and Terriault, P. Design of shape memory alloy actuators for morphing laminar wing with flexible extrados, J Mechanical Design, September 2009, 31, (9), 091006.Google Scholar
32. Brailovski, V., Terriault, P., Coutu, D., Georges, T., Morellon, E., Fischer, C. and Berube, S. Morphing laminar wing with flexible extrados powered by shape memory alloy actuators, 2008, ASME Conference Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS), Paper 337, Ellicott City, USA.Google Scholar
33. Coutu, D., Brailovski, V., Terriault, P. and Fischer, C. Experimental validation of the 3D numerical model for an adaptive laminar wing with flexible extrados, 2007, 18th International Conference of Adaptive Structures and Technologies, 3-5 October 2007, Ottawa, Ontario, Canada.Google Scholar
34. Coutu, D., Brailovski, V. and Terriault, P. Promising benefits of an active-extrados morphing laminar wing, AIAA J Aircr, 2009, 46, (2), pp 730731.Google Scholar
35. Hartl, D.J. and Lagoudas, D.C. Aerospace applications of shape memory alloys, Proceedings of the Institution of Mechanical Engineers, Part G: J Aerospace Eng, April 2007, 221, (4), pp 535552.Google Scholar
36. Khandelwal, A. and Buravalla, V. Models for shape memory alloy behavior: An overview of modeling approaches, Int J Structural Changes in Solids – Mechanics and Applications, December 2009, 1, (1), pp 111148.Google Scholar
37. Liu, S.H., Huang, T.S. and Yen, J.Y. Tracking control of shape-memory-alloy actuators based on self-sensing feedback and inverse hysteresis compensation, Sensors, 2010, 10, pp 112127.Google Scholar
38. Kapps, M. Smart-material mechanisms as actuation alternatives for aerospace robotics and automation, 2006, International Space Development Conference, ISDC06, pp 19, 4-7 May 2006, Los Angeles, USA.Google Scholar
39. Dutta, S.M., Ghorbel, F.H. and Dabney, J.B. Modeling and control of a shape memory alloy actuator, 2005, IEEE International Symposium on Intelligent Control, pp 10071012, 27-29 June 2005, Limassol, Cyprus.Google Scholar
40. DeFaria, C.T., Borduqui, H.G., Cavalini, A.A. and Lopes, V. Active control position using shape memory alloys, 2009, IMAC-XXVII, 9-12 February 2009, p 8, Orlando, FL, USA.Google Scholar
41. Terriault, P., Viens, F. and Brailovski, V. Non-isothermal finite element modeling of a shape memory alloy actuator using ANSYS, Computational Materials Science, July 2006, 36, (4), pp 397410.Google Scholar
42. Tomescu, B. On the Use of Fuzzy Logic to Control Paralleled dc-dc Converters, Dissertation, October, 2001, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.Google Scholar
43. Corcau, J.I. and Stoenescu, E. Fuzzy logic controller as a power system stabilizer, Int J Circuits, Systems and Signal Processing, 2007, 3, (1), pp 266273.Google Scholar
44. Jantzen, J. Tuning of fuzzy PID controllers, September 1998, Technical Report 98-H871, Department of Automation, Technical University of Denmark.Google Scholar
45. Kumar, V., Rana, K.P.S. and Gupta, V. Real-time performance evaluation of a fuzzy PI + fuzzy PD controller for liquid-level process, Int J Intelligent Control and Systems, June 2008, 13, (2), pp 8996.Google Scholar
46. Mahfouf, 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