Skip to main content
SpringerLink
Log in

Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system)

  • Research Article
  • Published:
Central European Journal of Engineering

Abstract

The helicopter dynamic includes nonlinearities, parametric uncertainties and is subject to unknown external disturbances. Such complicated dynamics involve designing sophisticated control algorithms that can deal with these difficulties. In this paper, a type 2 fuzzy logic PID controller is proposed for TRMS (twin rotor mimo system) control problem. Using triangular membership functions and based on a human operator experience, two controllers are designed to control the position of the yaw and the pitch angles of the TRMS. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Tee K. P., Ge S. S. and Tay F. E. H., Adaptive neural network control for helicopters in vertical flight, IEEE Trans. on Control Systems Technology 16(4), 2008, 753–762

    Article  Google Scholar 

  2. Wen P. and Lu T. W., Decoupling Control of a twin rotor MIMO system using robust deadbeat control technique, IET Control Theory and Applications 2(11), 2008, 999–1007

    Article  Google Scholar 

  3. Juang J. G., Huang M. T., and Liu W. K., PID control using presearched genetic algorithms for a MIMO system, IEEE Trans. on Systems, Man and Cybernetics: Part C 38(5), 2008, 716–727

    Article  Google Scholar 

  4. L’pez-Martinez M., Vivas C., and Ortega M. G., A multivariable nonlinear H ∞ controller for a laboratory helicopter, IEEE Conf. on European Control, 2005, 4065–4070

    Google Scholar 

  5. Rahideh A., Shaheed M. H. and Bajodah A. H., Adaptive Nonlinear Model Inversion Control of a Twin Rotor System Using Artificial Intelligence, IEEE Conf on Control Applications, 2007, 898–903

    Google Scholar 

  6. Juang J. G., Lin R. W., and Liu W. K., Comparison of classical control and intelligent control for a MIMO cystem, Applied Mathematics and Computation 205(2), 2008, 778–791

    Article  MATH  MathSciNet  Google Scholar 

  7. Utkin V., Variable structure systems with sliding modes, IEEE Trans. On Automatic Control 22(2), 1977, 212–222

    Article  MATH  MathSciNet  Google Scholar 

  8. Hung J. Y., Gao W. and Hung J. C., Variable structure control: a survey, IEEE Trans on Industrial Electronics 40(1), 1993, 2–22

    Article  Google Scholar 

  9. Zhang H., Shi Y., Mehr A. S., On H ∞ Filtering for Discrete-Time Takagi-Sugeno Fuzzy Systems, IEEE Transactions on Fuzzy Systems 20(2) 2012, 396–401

    Article  Google Scholar 

  10. Zhang H., Shi Y., Liu M., Cal H ∞ Step Tracking Control for Networked Discrete-Time Nonlinear Systems With Integral and Predictive Actions, IEEE Transactions on Industrial Informatics 9(1), 2013, 337–345

    Article  Google Scholar 

  11. Zhang H., Shi Y. and Mu B., Optimal HâĹđ-Based Linear-Quadratic Regulator Tracking Control for Discrete-Time Takagi-Sugeno Fuzzy Systems With Preview Actions, ASME Transactions, Journal of Dynamic Systems, Measurement and Control 135(4), 2013, 044501

    Article  Google Scholar 

  12. Shi Y., Zhang H., Wang J., On Energy-to-peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach, IEEE Transactions on Fuzzy Systems, DOI:10.1109/TFUZZ.2013.2250291, accepted and in press, 2013.

    Google Scholar 

  13. Tao C. W., Taurb J. S. and Chen Y. C., Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system, Fuzzy Sets and Systems 161(1), 2010, 2081–2103

    Article  MATH  MathSciNet  Google Scholar 

  14. Tao C. W., Taur J. S., Chang Y. H., Chang C. W., A Novel Fuzzy Sliding and Fuzzy Integral Sliding Controller for the Twin Rotor Multi-Input Multi-Output System, IEEE Trans. on Fuzzy Systems 18(4), 2010, 1–12

    Google Scholar 

  15. Rahideh A., Shaheed M. H., Bajodah A. H., Neural network based adaptive nonlinear model inversion control of a twin rotor system in real time, 7th IEEE International Conference on Cybernetic Intelligent Systems, 2008, 1–6

    Google Scholar 

  16. Omar M., Zaidan M. A. and Tokhi M. O., Dynamic modelling and control of a twin-rotor system using adaptive neuro-fuzzy inference system techniques, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226(7), July 2012, 787–803

    Article  Google Scholar 

  17. Shaik F. A. and Purwar S., A Nonlinear State Observer Design for 2 — DOF Twin Rotor System Using Neural Networks, IEEE Conf on Advances in Computing, Control, and Telecommunication Technologies, 2009, 15–19

    Google Scholar 

  18. Hagras H. A., A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst 12(4), 2004, 524–539

    Article  Google Scholar 

  19. Feedback Instruments Ltd., Twin Rotor MIMO System Advanced Technique Manual 33-949S, E. Sussex, England, 2002

    Google Scholar 

  20. Mendel J. M., Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper Saddle River, NJ, first edition, 2001

    Google Scholar 

  21. Mendel J. M., John R. I., and Liu F., Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems 14(6), 2006, 808–818

    Article  Google Scholar 

  22. Liang Q. and Mendel J. M., Interval type-2 fuzzy logic systems: theory and design, IEEE Transactions on Fuzzy Systems 8(5), 2000, 535–550

    Article  Google Scholar 

  23. Mendel J. M., Type-2 fuzzy sets: some question and answers, IEEE Connections, Newsletter of the IEEE Neural Networks Society 1, 2003, 10–13

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LASS Laboratory, Department of Electronics, Faculty of Technology, University of Msila, Msila, Algeria

    Samir Zeghlache & Djamel Saigaa

  2. SET laboratory Department of Electronics, Faculty of Engineering Sciences, University of Blida, Msila, Algeria

    Kamel Kara

  3. LMSE Laboratory, Department of Electrical Engineering, Faculty of Sciences and Technology, University of Biskra, Biskra, Algeria

    Djamel Saigaa

Authors
  1. Samir Zeghlache
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamel Kara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Djamel Saigaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samir Zeghlache.

About this article

Cite this article

Zeghlache, S., Kara, K. & Saigaa, D. Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system). cent.eur.j.eng 4, 303–315 (2014). https://doi.org/10.2478/s13531-013-0157-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s13531-013-0157-y

Keywords

Search

Navigation