Skip to main content

Advertisement

SpringerLink
Log in

Parameters tuning of a quadrotor PID controllers by using nature-inspired algorithms

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

This paper aims to investigate the control of a quadrotor by PID controller. The mathematical model is derived from Euler–Lagrange approach. Due to nonlinearities, coupling and under-actuation constraints, the model imposes difficulties to generate its controller by using classic ways. Firstly, we have designed a control structure which weakens the couplings and permits to develop a decentralized control. Secondly, in order to get the optimal path tracking, the controllers’ parameters were tuned by stochastic nature-inspired algorithms; Genetic Algorithm, Evolution Strategies, Differential Evolutionary and Cuckoo Search. A comparison study between these algorithms according to the path tracking is carried out by implementing simulations under MATLAB/Simulink. The results show the efficiency of the proposed strategy where the optimization algorithms achieve good performance with a slight difference between the indicate techniques.

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

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
Fig. 17

Similar content being viewed by others

References

  1. Yang Y, Yan Y (2015) Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control. Aerosp Sci Technol 54:208–217

    Article  Google Scholar 

  2. Bouabdallah S (2007) Design and control of quadrotors with application to autonomous flying. PhD thesis, EPFL, Lausanne, Switzerland

  3. Chen F, Zhang K, Wang Z, Tao G, Jiang G (2015) Trajectory tracking of a quadrotor with unknown parameters and its fault-tolerant control via sliding mode fault observer. Proc Inst Mech Eng Part I J Syst Control Eng 229(4):279–292

    Google Scholar 

  4. Xiong JJ, Zheng EH (2014) Position and attitude tracking control for quadrotor UAV. ISA Trans 53(3):725–731

    Article  Google Scholar 

  5. Voos H (2009) Nonlinear control of a quadrotor micro-UAV using feedback-linearization. In: IEEE international conference on mechatronics, Malaga, Spain, April 14

  6. Cao N, Lynch AF (2016) Inner-outer loop control for quadrotor UAVs with input and state constraints. IEEE Trans Control Syst Technol 24(5):1797–1804

    Article  Google Scholar 

  7. Jia Z, Yu J, Mei Y, Chen Y, Shen Y, Ai X (2017) Integral backstepping sliding mode control for quadrotor helicopter under external uncertain disturbances. Aerosp Sci Technol 68:299–307

    Article  Google Scholar 

  8. Raffo GV, Ortega MG, Rubio FR (2015) Robust nonlinear control for path tracking of a quad-rotor helicopter. Asian J Control 17(1):142–156

    Article  MathSciNet  Google Scholar 

  9. Bouabdallah S, Noth A, and Siegwart R (2004) PID vs LQ control techniques applied to an indoor micro quadrotor. In: 2004 IEEE/RSJ international conference on intelligent robots and systems, Sendal, Japan, September 28

  10. Kada B, Ghazzawi Y (2011) Robust PID controller design for an UAV flight control system. In: The world congress on engineering and computer sciences, San Francisco, USA, October 19

  11. Zarafshan P, Moosavian SB, Moosavian SAA, Bahrami M (2008) Optimal control of an aerial robot. In: 2008 IEEE/ASME international conference on advanced intelligent mechatronics, Xi’an, China, July 2

  12. Jiang F, Pourpanah F, Hao Q (2019) Design, implementation and evaluation of a neural network based quadcopter UAV system. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2019.2905808

    Article  Google Scholar 

  13. Astrom KJ, Hagglund T (1988) Automatic tuning of PID controllers. Instrum Society of America, Pennsylvania

    MATH  Google Scholar 

  14. Sahu RK, Panda S, Chandra-Sekhar GT (2015) A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems. Int J Electr Power Energy Syst 64:880–893

    Article  Google Scholar 

  15. Mandava RK, Vundavilli PR (2019) An optimal PID controller for a biped robot walking on flat terrain using MCIWO algorithms. Evolut Intell 12(1):33–48

    Article  Google Scholar 

  16. Rajarathinam K, Gomm JB, Yu DL, Abdelhadi AS (2016) PID controller tuning for a multivariable glass furnace process by genetic algorithm. Int J Autom Comput 13(1):64–72

    Article  Google Scholar 

  17. Salem A, Hassan MAM, Ammar ME (2014) Tuning PID controllers using artificial intelligence techniques applied to DC-motor and AVR system. Asian J Eng Technol 2(2):129–138

    Google Scholar 

  18. Sahoo BP, Panda S (2018) Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustain Energy Grids Netw 16:278–299

    Article  Google Scholar 

  19. Sivalingam R, Chinnamuthu S, Dash SS (2017) A hybrid stochastic fractal search and local unimodal sampling based multistage PDF plus (1 + PI) controller for automatic generation control of power systems. J Frankl Inst 354(12):4762–4783

    Article  MathSciNet  Google Scholar 

  20. Sivalingam R, Chinnamuthu S, Dash SS (2017) A modified whale optimization algorithm-based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems. Automatika 58(4):410–421

    Article  Google Scholar 

  21. Sahu PC, Prusty RC, Panda S (2019) Stability analysis in RECS-integrated multi-area AGC system with SOS algorithm based fuzzy controller. In: Behera H, Nayak J, Naik B, Abraham A (eds) Computational intelligence in data mining. Advances in intelligent systems and computing, vol 711. Springer, Singapore, pp 225–235

    Google Scholar 

  22. Fister D, Fister I Jr, Fister I, Safaric R (2016) Parameter tuning of PID controller with reactive nature-inspired algorithms. Robot Auton Syst 62:408–422

    Google Scholar 

  23. Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Boston

    Book  Google Scholar 

  24. Rechenberg I (1973) Evolutionstrategie: optimieruna technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog-Verlag, Stuttgart

    Google Scholar 

  25. Hansen N, Arnold DV, Auger A (2015) Evolution strategies. In: Kacprzyk J, Pedrycs W (eds) Springer handbook of computational intelligence. Springer, Heidelberg, pp 871–898

    Chapter  Google Scholar 

  26. Storn RM, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  27. Price K, Storn RM, Lampinen JA (2005) Differential evolution: a survey of the state-of-the-art. Springer, Berlin

    MATH  Google Scholar 

  28. Yang XS, Deb S (2009) Cuckoo search via Lèvy flights. In: World congress on nature and biologically inspired computing, Coimbatore, India, December 9

  29. Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, London

    MATH  Google Scholar 

  30. Spong MW, Hutchinson S, Vidyasagar M (2006) Robot modeling and control. Wiely, New York

    Google Scholar 

  31. Carrillo L, Lopez A, Lozano R, Pégard C (2013) Quad rotorcraft control. Springer, London

    Book  Google Scholar 

  32. Elbes M, Alzubi S, Kanan T, Al-Fuqaha A, Hawashin B (2019) A survey on particle swarm optimization with emphasis on engineering and network applications. Evolut Intell. https://doi.org/10.1007/s12065-019-00210-z

    Article  Google Scholar 

  33. Sahib MA, Ahmed BS (2013) A new multiobjective performance criterion used in PID tuning optimization algorithms. J Adv Res 7(1):125–134

    Article  Google Scholar 

  34. Rhimian MA, Tavazoei MS (2014) Improving integral square error performance with implementable fractional-order PI controllers. Optim Control Appl Methods 35(3):303–323

    Article  MathSciNet  Google Scholar 

  35. Shahemabadi AR, Noor SBM, Taip FS (2013) Analytical formulation of the integral square error for linear stable feedback control system. In: 2013 IEEE international conference on control system, computing and engineering, Penang, Malaysia, November 29

  36. Jain T, Nigam MJ (2008) Optimization of PD-PI controller using swarm intelligence. Int J Comput Cognit 6(4):55–59

    Google Scholar 

  37. Wright A (1991) Genetic algorithms for real parameter optimization. Morgan Kaufmann, San Mateo

    Book  Google Scholar 

  38. Ranjitham G, Shankar-Kumar KR (2016) Large scale multiple-input multiple-output (LS-MIMO) detection using genetic cat swarm optimization. Int J Adv Eng Technol 7(2):536–541

    Google Scholar 

  39. Yeo BK, Lu Y (1999) Array failure correction with a genetic algorithm. IEEE Trans Antennas Propag 47(5):823–828

    Article  Google Scholar 

  40. Abdou L, Soltani F (2008) OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies. Signal Image Video Process 2(2):155–167

    Article  Google Scholar 

  41. Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669

    Article  Google Scholar 

  42. Ouaarab A, Ahiod B, Yang XS (2014) Improved and discrete cuckoo search for solving the travelling salesman problem. In: Yang XS (ed) Cuckoo search and firefly algorithm theory and applications. Springer, Cham, pp 63–84

    Chapter  Google Scholar 

  43. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  Google Scholar 

  44. Acampora G, Ishibuchi H, Vitiello A (2014) A comparison of multi-objective evolutionary algorithms for the ontology meta-matching problem. In: 2014 IEEE congress on evolutionary computation, Beijing, China, July 6

  45. Klempka R, Filipowicz B (2017) Comparison of using the genetic algorithm and cuckoo search for multicriteria optimisation with limitation. Turk J Electr Eng Comput Sci 25(2):1300–1310

    Article  Google Scholar 

  46. Rout B, Pati BB, Panda S (2018) Modified SCA algorithm for SSSC damping controller design in power system. ECTI Trans Electr Eng Electron Commun 16(1):46–63

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Energy Systems Modeling Laboratory, Electrical Engineering Department, University of Biskra, Biskra, Algeria

    Seif-El-Islam Hasseni & Hossam-Eddine Glida

  2. Identification, Command, Control and Communication Laboratory, Electrical Engineering Department, University of Biskra, Biskra, Algeria

    Latifa Abdou

Authors
  1. Seif-El-Islam Hasseni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Latifa Abdou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossam-Eddine Glida
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seif-El-Islam Hasseni.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hasseni, SEI., Abdou, L. & Glida, HE. Parameters tuning of a quadrotor PID controllers by using nature-inspired algorithms. Evol. Intel. 14, 61–73 (2021). https://doi.org/10.1007/s12065-019-00312-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-019-00312-8

Keywords

Search

Navigation