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Novel morphing wing actuator control-based Particle Swarm Optimisation

Published online by Cambridge University Press:  26 September 2019

S. Khan ,
T. L. Grigorie ,
R. M. Botez Open the ORCID record for R. M. Botez [Opens in a new window] ,
M. Mamou  and
Y. Mébarki
S. Khan
Affiliation:
École de Technologie Supérieure, Montréal, Québec, Canada
T. L. Grigorie
Affiliation:
École de Technologie Supérieure, Montréal, Québec, Canada Military Technical Academy“Ferdinand I”, Bucharest, Romania
R. M. Botez*
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
*
[email protected]
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Abstract

The paper presents the design and experimental testing of the control system used in a new morphing wing application with a full-scaled portion of a real wing. The morphing actuation system uses four similar miniature brushless DC (BLDC) motors placed inside the wing, which execute a direct actuation of the flexible upper surface of the wing made from composite materials. The control system of each actuator uses three control loops (current, speed and position) characterised by five control gains. To tune the control gains, the Particle Swarm Optimisation (PSO) method is used. The application of the PSO method supposed the development of a MATLAB/Simulink® software model for the controlled actuator, which worked together with a software sub-routine implementing the PSO algorithm to find the best values for the five control gains that minimise the cost function. Once the best values of the control gains are established, the software model of the controlled actuator is numerically simulated in order to evaluate the quality of the obtained control system. Finally, the designed control system is experimentally validated in bench tests and wind-tunnel tests for all four miniature actuators integrated in the morphing wing experimental model. The wind-tunnel testing treats the system as a whole and includes, besides the evaluation of the controlled actuation system, the testing of the integrated morphing wing experimental model and the evaluation of the aerodynamic benefits brought by the morphing technology on this project. From this last perspective, the airflow on the morphing upper surface of the experimental model is monitored by using various techniques based on pressure data collection with Kulite pressure sensors or on infrared thermography camera visualisations.

Keywords

Morphing wingActuation systemBLDC motorControl tuningParticle Swarm OptimisationExperimental modelWind-tunnel testing
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1271 , January 2020 , pp. 55 - 75
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

United States Government Accountability Office. AVIATION: Impact of fuel price increases on the aviation industry, Report to Congressional Committees GAO-14-331, 25 September 2014, Washington, DC, US.Google Scholar
Popov, A-V., Grigorie, T.L., Botez, R-M., Mamou, M. and Mébarki, Y. Real time morphing wing optimization validation using wind-tunnel tests, J Aircr, 2010, 47, pp 1346–1355.CrossRefGoogle Scholar
Skillen, M.D. and Crossley, W.A. Modeling and Optimization for Morphing Wing Concept Generation, NASA/CR-2007-214860, March 2007, Langley Research Center, Hampton, Virginia, US.Google Scholar
Yang, J., Sartor, P., Cooper, J.E. and Nangia, R.K. Morphing Wing Design for Fixed Wing Aircraft, 2015, AIAA Science and Technology Forum (SciTech), Kissimmee, Florida, US.CrossRefGoogle Scholar
Sleesongsom, S., Bureerat, S. and Tai, K. Aircraft morphing wing design by using partial topology optimization, Struct Multidiscipl Optim, December 2013, 48, (6), pp 1109–1128.CrossRefGoogle Scholar
De Gaspari, A. and Ricci, S. Knowledge-based shape optimization of morphing wing for more efficient aircraft, Int J Aerosp Eng, 2015, 2015, pp. 1–19.CrossRefGoogle Scholar
Zhoujie Lyu, Z. and Martins, J.R.R.A. Aerodynamic shape optimization of an adaptive morphing trailing edge wing, J Aircr, 2015, 52, (6), pp 1951–1970.Google Scholar
Gamboa, P., Vale, J., Lau, F.J.P. and Suleman, A. Optimization of a morphing wing based on coupled aerodynamic and structural constraints, AIAA J, September 2009, 47, (9), pp. 2087–2104.CrossRefGoogle Scholar
Fincham, J.H.S. and Friswell, M.I. Aerodynamic optimisation of a camber morphing aerofoil, Aerosp Sci Technol, June 2015, 43, pp 245–255.CrossRefGoogle Scholar
Molinari, G., Quack, M., Dmitriev, V., Morari, M., Jenny, P. and Ermanni, P. Aero-structural optimization of morphing airfoils for adaptive wings. J Intell Mater Syst Struct, July 2011, 22, pp 1075–1089.CrossRefGoogle Scholar
Namgoong, H., Crossley, W.A. and Lyrintzis, A.S., Aerodynamic optimization of a morphing airfoil using energy as an objective, AIAA J, September 2007, 45, (9), pp 2113–2124.CrossRefGoogle Scholar
Woods, B.K.S. and Friswell, M.I. The adaptive aspect ratio morphing wing: Design concept and low fidelity skin optimization, Aerosp Sci Technol, 2015, 42, pp 209–217.CrossRefGoogle Scholar
Usher, T.D., Ulibarri, K.R. Jr. and Camargo, G.S. Piezoelectric microfiber composite actuators for morphing wings, ISRN Mater Sci, 2013, 2013, pp 1–8.CrossRefGoogle Scholar
Bilgen, O. and Friswell, M.I. Piezoceramic composite actuators for a solid-state variable-camber wing, J Intell Mater Syst Struct, 2014, 25, (7), pp 806–817.CrossRefGoogle Scholar
Ohanian, O.J. III, Karni, E.D, Olien, C.C, Gustafson, E.A, Kochersberger, K.B, Gelhausen, P.A. and Brown, B.L. Piezoelectric composite morphing control surfaces for unmanned aerial vehicles, Proc. SPIE 7981, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2011, 18 April 2011, San Diego, CA, US.CrossRefGoogle Scholar
Debiasi, M., Bouremel, Y., Khoo, H. H., Luo, S. C. and Tan, E.Z. Shape change of the upper surface of an airfoil by macro fiber composite actuators, AIAA Paper 2011-3809, 29th AIAA Applied Aerodynamics Conference, June 2011, Honolulu, HI, US.CrossRefGoogle Scholar
Vos, R., Barrett, R., de Breuker, R. and Tiso, P. Post-buckled precompressed elements: A new class of control actuators for morphing wing UAVs, Smart Mat Struct, 2007, 16, pp 919–926.CrossRefGoogle Scholar
Bil, C., Massey, K. and Abdullah, E.J. Wing morphing control with shape memory alloy actuators, J Intell Mater Syst Struct, 2013, 24, (7), pp 879–898.CrossRefGoogle Scholar
Kang, W.R., Kim, E.H., Jeong, M.S. and Lee, I. Morphing wing mechanism using an SMA wire actuator, Int J Aeronaut Space Sci, 2012, 13, (1), pp 58–63.CrossRefGoogle Scholar
Karagiannis, D., Stamatelos, D., Spathopoulos, T., Solomou, A., Machairas, T., Chrysohoidis, N., Saravanos, D. and Kappatos, V. Airfoil morphing based on SMA actuation technology, Aircr Eng Aerosp Technol: Int J, 2014, 86, (4), pp 295–306.CrossRefGoogle Scholar
Barbarino, S., Pecora, R., Lecce, L., Concilio, A., Ameduri, S. and De Rosa, L. Airfoil structural morphing based on S.M.A. actuator series: Numerical and experimental studies, J Intell Mater Syst and Struct, July 2011, 22, (10), pp 987–1004.CrossRefGoogle Scholar
Botez, R. M., Molaret, P. and Laurendeau, E. Laminar flow control on a research wing project presentation covering a three-year period, CASI Aircraft Design and Development Symposium, 24–26 April 2007, Toronto, Canada.Google Scholar
Grigorie, T.L., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. On–off and proportional-integral controller for a morphing wing. Part 1: Actuation mechanism and control design, Proc Inst Mech Eng, Part G: J Aerosp Eng, 2012, 226, (2), pp 131–145.CrossRefGoogle Scholar
Grigorie, T.L., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. On–off and proportional-integral controller for a morphing wing. Part 2: Control validation–numerical simulations and experimental tests, Proc Inst Mech Eng, Part G: J Aerosp Eng, 2012, 226, (2), pp 146–162.CrossRefGoogle Scholar
Grigorie, T.L., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. 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, Aeronaut J, May 2012, 116, (1179), pp 433–450.CrossRefGoogle Scholar
Grigorie, T.L., Popov, A.V., Botez, R.M., Mamou, M. and Mébarki, Y. A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy. Part 2: Controller implementation and validation, Aeronaut J, May 2012, 116, (1179), pp 451–465.CrossRefGoogle Scholar
Grigorie, T.L., Popov, A.V. and Botez, R.M. Control Strategies for an Experimental Morphing Wing Model, AIAA Aviation 2014, AIAA Atmospheric Flight Mechanics (AFM) Conference, 16–18 June 2014, Atlanta, GA, US.CrossRefGoogle Scholar
Popov, A.V., Grigorie, T.L., Botez, R.M., Mamou, M. and Mébarki, Y. Closed-loop control validation of a morphing wing using wind tunnel tests, J Aircr, 2010, 47, (4), pp 1309–1317.CrossRefGoogle Scholar
Popov, A.V., Grigorie, T.L., Botez, R.M., Mamou, M. and Mébarki, Y. Modeling and testing of a morphing wing in open-loop architecture, J Aircr, 2010, 47, (3), pp 917–923.CrossRefGoogle Scholar
Koreanschi, A., Sugar-Gabor, O. and Botez, R.M. Numerical and experimental validation of a morphed wing geometry using Price-Padoussis wind tunnel testing, Aeronaut J, 2016, 120, (1227), pp 757–795.CrossRefGoogle Scholar
Sugar Gabor, O., Simon, A., Koreanschi, A. and Botez, R. M. Aerodynamic performance improvement of the UAS-S4 Éhecatl morphing airfoil using novel optimization techniques, Proc Inst Mech Eng, Part G: J Aerosp Eng, 2016, 230, (7), pp 1164–1180.CrossRefGoogle Scholar
Kammegne Tchatchueng, M.J., Grigorie, T.L., Botez, R.M. and Koreanschi, A. Design and validation of a position controller in the Price-PaÏdoussis wind tunnel, IASTED Modeling, Simulation and Control conference, 17–19 February 2014, Innsbruck, Austria.CrossRefGoogle Scholar
Tchatchueng Kammegne, M.J., Grigorie, T.L., Botez, R.M. and Koreanschi, A. Design and wind tunnel experimental validation of a controlled new rotary actuation system for a morphing wing application, Proc Inst Mech Eng, Part G: J Aerosp Eng, January 2016, 230, pp 132–145.CrossRefGoogle Scholar
Amendola, G., Dimino, I., Magnifico, M. and Pecora, R. Distributed actuation concepts for a morphing aileron device. Aeronaut J, 2016, 120, (1231), pp 1365–1385.CrossRefGoogle Scholar
Arena, M., Amoroso, F., Pecora, R., Amendola, G., Dimino, I. and Concilio, A. Numerical and experimental validation of a full-scale servo-actuated morphing aileron model. Smart Mater Struct, 2018, 27, (10), pp 1–21.CrossRefGoogle Scholar
Koreanschi, A., Sugar-Gabor, O. and Botez, R.M. Drag optimization of a wing equipped with a morphing upper surface, Aeronaut J, 2016, 120, (1225), pp 473–493.CrossRefGoogle Scholar
Khan, S., Botez, R. M. and Grigorie, T.L. A new method for tuning PI gains for position control of BLDC motor-based wing morphing actuators, AIAA Modeling and Simulation Technologies Conference, 22–26 June 2015, Dallas, TX, US.Google Scholar
Kennedy, J. and Eberhart, R. Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks (ICNN), Australia, 1995, pp 1942–1948.Google Scholar
Venter, G. and Sobieszczanski-Sobieski, J. Multidisciplinary optimization a transport aircraft wing using particle swarm optimization, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia, 4–6 September 2002.CrossRefGoogle Scholar
Venter, G. and Sobieszczanski-Sobieski, J. Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization, Struct Multidiscip Optim, January 2004, 26, (1–2), pp 121–131.CrossRefGoogle Scholar
Blasi, L. and Del Core, G. Particle swarm approach in finding optimum aircraft configuration, J Aircr, March–April 2007, 44, (2), pp 679–683 CrossRefGoogle Scholar
Pontani, M. and Conway, B.A. Particle swarm optimization applied to space trajectories, J Guid Control Dynam, September–October 2010, 33, (5), pp 1429–1441 CrossRefGoogle Scholar
Grant, M.J. and Mendeck, G.F. Mars science laboratory entry optimization using particle swarm methodology, AIAA Atmospheric Flight Mechanics Conference and Exhibit, 20–23 August 2007, Hilton Head, South Carolina, US.CrossRefGoogle Scholar
Ghamry, K.A., Kamel, M.A. and Zhang, Y.M. Multiple UAVs in forest fire fighting mission using particle swarm, Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS’17), 13–16 June 2017, Miami, US.CrossRefGoogle Scholar
Kamel, M.A., Yu, X. and Zhang, Y.M. Fault-tolerant cooperative control of WMRs under actuator faults based on particle swarm optimization, 3rd International Conference on Control and Fault-Tolerant Systems (SysTol’16), 7–9 September 2016, Barcelona, Spain.CrossRefGoogle Scholar
Kamel, M.A., Yu, X. and Zhang, Y.M. Real-time optimal formation reconfiguration of multiple wheeled mobile robots based on particle swarm optimization, 35th Chinese Control Conference (CCC2016), 27–29 July 2016, Chengdu, China.CrossRefGoogle Scholar
Hassan, R., Cohanim, B. and de Weck, O. A comparison of particle swarm optimization and the genetic algorithm, 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Structures, Structural Dynamics, and Materials and Co-located Conferences, 18–21 April 2005, Austin, Texas, US.CrossRefGoogle Scholar
Van den Bergh, F. An Analysis of Particle Swarm Optimizers, PhD thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa, 2002.Google Scholar
Poli, R., Kennedy, J. and Blackwell, T. Particle swarm optimization. An overview, Swarm Intell, June 2007, 1, (1), pp 33–57.CrossRefGoogle Scholar