Hostname: page-component-89b8bd64d-n8gtw Total loading time: 0 Render date: 2026-05-07T11:05:19.066Z Has data issue: false hasContentIssue false
  • English
  • Français

Fuzzy-PID side-stick force control for flight simulation

Published online by Cambridge University Press:  18 May 2016

K. Fellah ,
M. Guiatni ,
A.K. Ournid  and
M.A. Boulahlib
K. Fellah*
Affiliation:
Control Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria
M. Guiatni
Affiliation:
Control Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria
A.K. Ournid
Affiliation:
Control Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria
M.A. Boulahlib
Affiliation:
Control Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria
*
fellahkh@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we present a new force-feedback side-stick which has been developed and integrated into a research flight simulator. The developed 2 Degrees of Freedom (DOF) force-feedback joystick, as a kind of haptic device, provides two-way communication in both position and force, and allows users to interact with the simulation system. It has been designed by considering the main factors in designing a general use force-feedback device. Thus, the design must allow the restitution of aerodynamic forces onto the hand of the pilot. This is an important feature, which gives the pilot the ‘natural feel’ of traditional mechanical aircraft control. In order to provide the force feedback to enhance the realism of the simulation, we added the necessary software using Commercial-Off-the-Shelf (COTS) solutions (Microsoft Flight Simulator Software (MSFS)) and built-in data structure and methods. Thus, the main contribution of this paper concerns the design and implementation of an automatic controller based on fuzzy logic systems. It is not simply designing a force-feedback stick for flight simulation: we proposed a novel control principles and more importantly completely new approach to compute in real-time force feedback on the stick based on pilot knowledge that avoids the use of complex aerodynamics equations with unknown parameters. To our best knowledge, this work is the first to propose the integration of fuzzy logic force controller in flight simulation for creating force feedback. Results using the overall simulation are presented and evaluated and interesting sensations have been recorded.

Keywords

Commercial-off-the-shelfforce cueingflight simulationforce-feedback side-stickPID-fuzzy logic control

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1227 , May 2016 , pp. 845 - 872
Copyright
Copyright © Royal Aeronautical Society 2016 

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.)

Article purchase

Temporarily unavailable

References

REFERENCES

1. Hoedemaeker, M. and Brookhuis, K. Behavioural adaptation to driving with an adaptive cruise control (ACC), Transportation Research Part F, 1998, 1, (2), pp 95–106.Google Scholar
2. Kemeny, A. and Panerai, F. Evaluating perception in driving simulation experiments, Trends in Cognitive Sciences, 2003, 7, (1), pp 31–37.Google Scholar
3. Katzourakis, D., de Winter, J., de Groot, S. and Happee, R. Driving simulator parameterization using double-lane change steering metrics as recorded on five modern cars, Simulation Modeling Practice and Theory, 2012, 26, pp 96–112.Google Scholar
4. Clari, M.S.V., Ruigrok, R.C., Heesbeen, B.W. and Groeneweg, J. Research flight simulation of future autonomous aircraft operations, Proceedings of the Conference of European Aerospace Societies (CEAS), 2002, pp 1126–1134.Google Scholar
5. Hoekstra, J. The smart software-simple hardware concept for maximum flexibility in research flight simulation, Proceedings of the Conference of European Aerospace Societies (CEAS), vol. NLR-TP-96215, 1995, Amsterdam, the Netherlands.Google Scholar
6. Teufel, H., Nusseck, H., Beykirch, K., Butler, J., Kerger, M. and Blthoff, H. MPI motion simulator: Development and analysis of a novel motion simulator, AIAA Modeling and Simulation Technologies Conference and Exhibit, 20-23 August 2007, Hilton Head, South Carolina, US, pp 1–11.Google Scholar
7. Holzapfel, F., Sturhan, I. and Sachs, G. Low-cost pc based flight simulator for education and research, AIAA Modeling and Simulation Technologies Conference and Exhibit, no. 1, 5-8 August 2002, p 15–40.Google Scholar
8. Albert, M. R., Rehmann, J. and Mitman, R.D. A handbook of flight simulation fidelity requirements for human factors research, Technical Report, Crew System Ergonomics Information Analysis Center (CSERIAC), December 1995.Google Scholar
9. Mueller, R. Evolution of a simulator pilot force-feel system, AIAA Modeling and Simulation Technologies Conference, 2010, pp 1–16.Google Scholar
10. Puangmali, P., Althoefer, K., Seneviratne, L.D., Murphy, D. and Dasgupta, P. State-of-the-art in force and tactile sensing for minimally invasive surgery, IEEE Sensors J, 2008, 8, (4), pp 371–381.Google Scholar
11. Alaimo, S.M., Pollini, L., Magazz, A., Bresciani, J.P., Giordano, P.R., Innocenti, M. and Bulthoff, H.H. Preliminary evaluation of a haptic aiding concept for remotely piloted vehicles, EuroHaptics 2010, Part II, LNCS 6192, 2010, pp 418–425.Google Scholar
12. Lam, T., Mulder, M. and Van Paassen, M. Haptic feedback in UAV teleoperation with time delay, AIAA J of Guidance, Control, and Dynamics, 2008, 31, (6), pp 1728–1739.Google Scholar
13. Nam, Y. and Hong, S.K. Active stick control using frictional torque compensation, Sensors and Actuators, A, 2005, 117, pp 194–202.Google Scholar
14. Lin, W.-C. and Young, K.-Y. Design of force-reflection joystick system for VR-based simulation, J Information Science and Engineering, 2007, pp 1421–1436.Google Scholar
15. Baarspul, M. A review of flight simulation techniques, Progress in Aerospace Sciences, 1990.Google Scholar
16. Coiro, D., De Marco, A. and Nicolosi, F. Flight simulation environment of the university of naples and recent developments in control loading reproduction, Communications to SIMAI Congress, January 2007, 2, (1), pp 28–35.Google Scholar
17. Condomines, J., Defay, F. and Alazard, D. Robust impedance active control of flight control devices, 19th World Congress of t, 24-29 August 2014, Cape Town, South Africa, 2014, pp 8365–8371.Google Scholar
18. Guiatni, M., Ournid, A., Boulahlib, M.A. and Abane, A. Programmable force-feedback side-stick for flight simulation, 15th IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2012), 13-15 May 2012, Austria, pp 2526-2530.Google Scholar
19. Grigorie, L., Botez, R., Popov, A., Mamou, M. and Mebarki, 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, Aeronautical J, 2012, 116, (1179), pp 433–449.Google Scholar
20. Grigorie, R.L.T., Botez, A. Popov, Mamou, M. and Mebarki, 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, Aeronautical J, 2012, 116, (1179), pp 451–465.Google Scholar
21. Ghazi, G. and Botez, R. New robust control analysis methodology for lynx helicopter and Cessna Citation X aircraft using guardian maps, genetic algorithms and LQR theories combinations, AHS 70th Annual Forum and Technology Display, 20-22 May 2014, Montreal, Quebec, Canada.Google Scholar
22. Grigorie, L., Botez, R., Popov, A., Mamou, M. and Mebarki, Y. Adaptive neuro-fuzzy controllers for an open loop morphing wing system, Proceedings of the Institution of Mechanical Engineers - Part G: J of Aerospace Engineering, 2009, 223, pp 965–975.Google Scholar
23. Boughari, Y., Botez, R., Ghazi, G. and Theel, F. Evolutionary algorithms for robust Cessna Citation X flight control, SAE 2014 Aerospace Systems and Technology Conference, 23-25 September 2014, Cincinnati, Ohio, US.Google Scholar
24. Whalley, M. and Achache, M. Joint U.S./France investigation of helicopter flight envelope limit cueing, American Helicopter Society 52nd Annual Forum, 1996.Google Scholar
25. Coiro, D., De Marco, A. and Nicolosi, F. A 6dof flight simulation environment for general aviation aircraft with control loading reproduction, AIAA Modeling and Simulation Technologies Conference and Exhibit, 2007.Google Scholar
26. Sadraey, M. Ed. Aircraft Performance Analysis, 2009, VDM Verlag Dr. Muller. Google Scholar
27. Falkena, W., Borst, C. and Mulder, J. Investigation of practical flight envelope protection systems for small aircraft, AIAA Guidance, Navigation, and Control Conference AIAA 2010-7701, 2010, Toronto, Ontario, Canada, 2010.Google Scholar
28. Zheng, S., Zheng, S. and Han, J. Cots and design pattern based high fidelity flight simulator prototype system, Journal of Computers, January 2011, 6, (1), pp 28–35.Google Scholar
29. Microsoft Flight Simulator. Available at http://www.microsoft.com/games/flightsimulator/, May 2004.Google Scholar
30. Dowson, P. FS Modules. Available at: http://www.schiratti.com/dowson, September 09, 2013.Google Scholar
31. Laycock, S.D. and Day, A.M. Recent developments and applications of haptic devices, Computer Graphics, 2003, 22, (2), pp 117–132.Google Scholar
32. Baser, O., Konukseven, E.I. and Koku, B. 7 DOF haptic device design, EuroHaptics 2006, July 2006, pp 507-512.Google Scholar
33. Li, T. On the design and development of robotic mechanisms for laparoscopic surgery, Ph.D. dissertation, 2006, Simon Fraser University, Burnaby, British Columbia, Canada.Google Scholar
34. Birglen, L., Gosselin, C. and Pouliot, N. Shade, a new 3-dof haptic device, IEEE Transactions on Robotics and Automation, April 2002, 18, (2), pp 166–175.Google Scholar
35. Guiatni, M., Riboulet, V. and Kheddar, A. Design and evaluation of a haptic interface for interactive simulation of minimally-invasive surgeries, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2009), 14-17 July 2009, Singapore.Google Scholar
36. Tavakoli, M., Patel, R. and Moallem, M. Design issues in a haptics-based master-slave system for minimally invasive surgery, IEEE International Conference on Robotics and Automation, April 2004, New Orleans, Louisiana, US, pp 371-377.Google Scholar
37. Hayward, V. Toward a seven axis haptic device, International Conference on Intelligent Robots and Systems, IEEE Computer Society, Washington, DC, US, pp 31-33.Google Scholar
38. Hayward, V. and Astley, O.R. Performance measures for haptic interfaces, The 7th International Symposium on Robotics Research, 1996, Springer-Verlag, pp 195-207.Google Scholar
39. Kabadayi, S. Design of a six degrees of freedom haptic interface, Master’s thesis, Sabanci University, Istanbul, Turkey, 2006.Google Scholar
40. Walker, S. and Salisbury, J.K. Difference-based estimation of support friction, IEEE/RSJ International Conference on Intelligent Robots and Systems, 22-26 September 2008, pp 59-64.Google Scholar
41. Smith, L. Chemical engineering, Deskbook Issue, 1979, 88, (22), pp 11–39.Google Scholar
42. Astrom, K. and Hagglund, T. Eds. PID Controller - Theory, Design and Tuning, 2nd ed, 1995, Instrument Society of America, Research Triangle Park, North Carolina, US.Google Scholar
43. Sala, A., Guerra, T.-M. and Babuska, R. Perspectives of fuzzy systems and control, Fuzzy Sets and Systems, 2005, 156, (3), pp 432–444.Google Scholar
44. Feng, G. A survey on analysis and design of model-based fuzzy control systems, IEEE Transactions on Fuzzy Systems, 2006, 14, (5), pp 676–697.Google Scholar
45. Precup, R.-E. and Hellendoorn, H. A survey on industrial applications of fuzzy control, Computers in Industry, 2011, 62, (3), pp 213–226.Google Scholar
46. Yen, J. and Langari, R. Fuzzy Logic: Intelligence, Control and Information, 1999, Prentice-Hall.Google Scholar
47. Sepehri, N. and Lawrence, P. Fuzzy logic control of a teleoperated log loader machine, IEEE/RSJ International Conference on Intelligent Robots and Systems, 1998, pp 1571-1577.Google Scholar
48. Jantzen, J. Tuning of fuzzy PID controllers, Technical Report Number 98-H 871, September 1998, Technical University of Denmark, Department of Automation, Bldg 326, DK-2800 Lyngby, Denmark.Google Scholar
49. Jiang, T. and Li, Y. Generalized defuzzification strategies and their parameter learning procedures, IEEE Transactions on Fuzzy Systems, 1996, 4, (4), pp 64–71.Google Scholar