[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A fuzzy adaptive controller design for integrated guidance and control of a nonlinear model helicopter

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

A fuzzy adaptive sliding mode controller is presented in this research and implemented on a nonlinear helicopter model. An integrated guidance and control for a model helicopter which is flying behind a floating platform is considered in order to stabilise dynamics and track path, simultaneously. A fuzzy logic is designed to adaptively choose the best control parameters for the sliding mode controller and relieve the designer’s concern in choosing the parameter. A matrix consisted of fuzzy sliding mode parameters is used instead of a single parameter with the goal of enhancing controller tracking capability. The problem is simulated under different conditions and intense disturbances of an empirical model, while the performance is acceptable. Controller performance is compared, and a performance analysis is done on the selection of fuzzy membership functions using an optimisation method. Simulation results show the robustness, agility, stability and overall outperformance of the proposed controller.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability

Not applicable.

Code Availability

Not applicable.

Abbreviations

a :

Perturbed longitudinal flapping angle

A :

Longitudinal flapping angle

A :

Flapping stability derivatives

A :

System dynamics matrix

B :

Input matrix

DoF:

Degrees of freedom

e :

Error

g :

Gravity

I :

Identity matrix

ICA:

Imperialist competitive algorithm

IGC:

Integrated guidance and control

K :

Feedback gains matrix

LQR:

Linear–quadratic regulator

m :

Number of fuzzy rules

M :

Stability derivatives of pitch moment

MF:

Membership function

MIMO:

Multi-input multi-output

n :

Number of variables

NB:

Negative big

NS:

Negative small

PB:

Positive big

PID:

Proportional, integral and derivative

PS:

Positive small

q :

Perturbed pitch rate

Q :

Pitch rate

R m :

Radius of the main rotor

RMS:

Root mean square

s :

Transfer function variable

T :

Transformation matrix

u :

Perturbed forward speed

u :

Vector of inputs

U :

Forward speed

U∞ :

Wind mean speed

UAV:

Unmanned flying vehicle

w :

Perturbed vertical speed

W :

Vertical speed

x :

Horizontal displacement, position variable

x :

Vector of state variables

X:

Stability derivatives of force in x-direction

Z :

Vertical displacement

Z :

Stability derivatives of force in z-direction

ZO:

Zero

γ:

Sliding mode parameter

δ :

Deflection angle

θ :

Perturbed pitch angle

Θ :

Pitch angle

µ F :

Membership function

π:

Pi number

σ ω :

Turbulence intensity

τ f :

Rotor time constant

ω n :

White noise

~:

Difference between the desired and current value

col:

Collective

d :

Desired

i :

Input

j :

Loop counter

lon:

Longitudinal cyclic

References

  1. Alvarenga J, Vitzilaios NI, Valavanis KP et al (2015) Survey of unmanned helicopter model-based navigation and control techniques. J Intell Rob Syst 80(1):87–138

    Article  Google Scholar 

  2. Kendoul F (2012) Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J Field Robot 29(2):315–378

    Article  Google Scholar 

  3. Dong ZY, Liu SA, Liu C et al (2017) Modelling and robust control of an unmanned coaxial rotor helicopter with unstructured uncertainties. Adv Mech Eng 9(1):1–14

    Article  Google Scholar 

  4. Dong ZY, Liu SA, Liu J et al (2017) Flexible performance design of robust control using the linear matrix technique for a robotic coax-helicopter. Arab J Sci Eng 42(5):1783–1793

    Article  Google Scholar 

  5. Lan J, Patton RJ, Zhu X (2017) Integrated fault-tolerant control for a 3-DOF helicopter with actuator faults and saturation. IET Control Theory Appl 11(14):2232–2241

    Article  MathSciNet  Google Scholar 

  6. Thanapalan K (2017) Nonlinear controller design for a helicopter with an external slung load system. Syst Sci Control Eng 5(1):97–107

    Article  Google Scholar 

  7. Czerniak JM, Ewald D, Śmigielski G et al (2016) Optimization of fuel consumption in fire-fighting water capsule flights of a helicopter. In: Fidanova S (ed) Recent advances in computational optimization. Springer, Cham, pp 39–49

    Chapter  Google Scholar 

  8. Chen M, Shi P, Lim CC (2015) Adaptive neural fault-tolerant control of a 3-DOF model helicopter system. IEEE Trans Syst Man Cybern Syst 46(2):260–270

    Article  Google Scholar 

  9. Pedro JO, Mathe C (2015) Nonlinear direct adaptive control of quadrotor UAV using fuzzy logic technique. In: 10th Asian control conference (ASCC). IEEE, Kota Kinabalu, Malaysia, 31, pp 1–6

  10. Tang Y, Zhang H, Gong J (2015) Adaptive-fuzzy sliding-mode control for the attitude system of a quadrotor. In: Chinese automation congress (CAC). IEEE, Wuhan, China, pp 1075–1079

  11. Santoso F, Garratt MA, Anavatti SG (2016) Adaptive neuro-fuzzy inference system identification for the dynamics of the AR. drone quadcopter. In: International conference on sustainable energy engineering and application (ICSEEA). IEEE, Jakarta, Indonesia, pp 55–60

  12. Cheng D, Ning W, Meng JE (2016) Self-organizing adaptive robust fuzzy neural attitude tracking control of a quadrotor. In: 35th Chinese control conference (CCC). IEEE, Chengdu, China, pp 10724–10729

  13. Yan W, Huang J, Xu D (2016) Adaptive fuzzy tracking control for non-affine nonlinear yaw channel of unmanned aerial vehicle helicopter. Int J Adv Rob Syst 14(1):1–12

    Google Scholar 

  14. Glida HE, Abdou L, Chelihi A, et al. (2019) Optimal direct adaptive fuzzy controller based on bat algorithm for UAV quadrotor. In: 8th international conference on systems and control (ICSC). IEEE, Marrakesh, Morocco, pp 52–57

  15. Hu Y, Yang Y, Li S et al (2020) Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm. Aerosp Sci Technol 98(1):1–12

    Google Scholar 

  16. Shi X, Cheng Y (2020) Fuzzy adaptive sliding mode control for unmanned quadrotor. In: IEEE/ASME international conference on advanced intelligent mechatronics (AIM). IEEE, Boston, USA, pp 1654–1658

  17. Chaoui H, Yadav S, Ahmadi RS et al (2020) Adaptive interval type-2 fuzzy logic control of a three degree-of-freedom helicopter. Robotics 9(3):59–74

    Article  Google Scholar 

  18. Mahmoodabadi MJ, Rezaee Babak N (2020) Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor. J Vib Control 26(17–18):1574–1589

    Article  Google Scholar 

  19. Marvin T (2013) Sliding mode control of MIMO non-square systems via squaring matrix transforms. Thesis, Rochester Institute of Technology, USA

  20. Yang Y, Yan Y (2016) Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control. Aerosp Sci Technol 54(1):208–217

    Article  Google Scholar 

  21. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, Singapore, pp 4661–4667

  22. Kantue P (2011) Online parameter estimation of a miniature unmanned helicopter using neural network techniques. Ph.D. Thesis, University of the Witwatersrand, South Africa

  23. Ngo TD (2016) Constrained control for helicopter shipboard operations and moored ocean current turbine flight control. Ph.D. Dissertation, Virginia Tech, USA

Download references

Acknowledgements

Not applicable.

Funding

This paper is not funded by any organisation.

Author information

Authors and Affiliations

  1. Aerospace Engineering Department, K. N. Toosi University of Technology, Tehran, Iran

    Farhad Pakro & Amir Ali Nikkhah

Authors
  1. Farhad Pakro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Ali Nikkhah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Farhad Pakro and Amir Ali Nikkhah. The first draft of the manuscript was written by Farhad Pakro, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Farhad Pakro or Amir Ali Nikkhah.

Ethics declarations

Conflict of interest

There are no financial or non-financial interests in this research.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pakro, F., Nikkhah, A.A. A fuzzy adaptive controller design for integrated guidance and control of a nonlinear model helicopter. Int. J. Dynam. Control 11, 701–716 (2023). https://doi.org/10.1007/s40435-022-00993-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-022-00993-7

Keywords

Search

Navigation