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

  • Seif-El-Islam HasseniEmail author
  • Latifa Abdou
  • Hossam-Eddine Glida
Special Issue
  • 13 Downloads

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.

Keywords

Quadrotor PID controller Optimization Stochastic nature-inspired algorithm Evolutionary algorithms 

Notes

References

  1. 1.
    Yang Y, Yan Y (2015) Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control. Aerosp Sci Technol 54:208–217CrossRefGoogle Scholar
  2. 2.
    Bouabdallah S (2007) Design and control of quadrotors with application to autonomous flying. PhD thesis, EPFL, Lausanne, SwitzerlandGoogle Scholar
  3. 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):279–292CrossRefGoogle Scholar
  4. 4.
    Xiong JJ, Zheng EH (2014) Position and attitude tracking control for quadrotor UAV. ISA Trans 53(3):725–731CrossRefGoogle Scholar
  5. 5.
    Voos H (2009) Nonlinear control of a quadrotor micro-UAV using feedback-linearization. In: IEEE international conference on mechatronics, Malaga, Spain, April 14Google Scholar
  6. 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):1797–1804CrossRefGoogle Scholar
  7. 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 68:299–307CrossRefGoogle Scholar
  8. 8.
    Raffo GV, Ortega MG, Rubio FR (2015) Robust nonlinear control for path tracking of a quad-rotor helicopter. Asian J Control 17(1):142–156MathSciNetCrossRefGoogle Scholar
  9. 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 28Google Scholar
  10. 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 19Google Scholar
  11. 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 2Google Scholar
  12. 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 CrossRefGoogle Scholar
  13. 13.
    Astrom KJ, Hagglund T (1988) Automatic tuning of PID controllers. Instrum Society of America, PennsylvaniazbMATHGoogle Scholar
  14. 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 64:880–893CrossRefGoogle Scholar
  15. 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):33–48CrossRefGoogle Scholar
  16. 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):64–72CrossRefGoogle Scholar
  17. 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):129–138Google Scholar
  18. 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 16:278–299CrossRefGoogle Scholar
  19. 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):4762–4783MathSciNetCrossRefGoogle Scholar
  20. 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):410–421CrossRefGoogle Scholar
  21. 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 225–235CrossRefGoogle Scholar
  22. 22.
    Fister D, Fister I Jr, Fister I, Safaric R (2016) Parameter tuning of PID controller with reactive nature-inspired algorithms. Robot Auton Syst 62:408–422Google Scholar
  23. 23.
    Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, BostonCrossRefGoogle Scholar
  24. 24.
    Rechenberg I (1973) Evolutionstrategie: optimieruna technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog-Verlag, StuttgartGoogle Scholar
  25. 25.
    Hansen N, Arnold DV, Auger A (2015) Evolution strategies. In: Kacprzyk J, Pedrycs W (eds) Springer handbook of computational intelligence. Springer, Heidelberg, pp 871–898CrossRefGoogle Scholar
  26. 26.
    Storn RM, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefGoogle Scholar
  27. 27.
    Price K, Storn RM, Lampinen JA (2005) Differential evolution: a survey of the state-of-the-art. Springer, BerlinzbMATHGoogle Scholar
  28. 28.
    Yang XS, Deb S (2009) Cuckoo search via Lèvy flights. In: World congress on nature and biologically inspired computing, Coimbatore, India, December 9Google Scholar
  29. 29.
    Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, LondonzbMATHGoogle Scholar
  30. 30.
    Spong MW, Hutchinson S, Vidyasagar M (2006) Robot modeling and control. Wiely, New YorkGoogle Scholar
  31. 31.
    Carrillo L, Lopez A, Lozano R, Pégard C (2013) Quad rotorcraft control. Springer, LondonCrossRefGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. 33.
    Sahib MA, Ahmed BS (2013) A new multiobjective performance criterion used in PID tuning optimization algorithms. J Adv Res 7(1):125–134CrossRefGoogle Scholar
  34. 34.
    Rhimian MA, Tavazoei MS (2014) Improving integral square error performance with implementable fractional-order PI controllers. Optim Control Appl Methods 35(3):303–323MathSciNetCrossRefGoogle Scholar
  35. 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 29Google Scholar
  36. 36.
    Jain T, Nigam MJ (2008) Optimization of PD-PI controller using swarm intelligence. Int J Comput Cognit 6(4):55–59Google Scholar
  37. 37.
    Wright A (1991) Genetic algorithms for real parameter optimization. Morgan Kaufmann, San MateoCrossRefGoogle Scholar
  38. 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):536–541Google Scholar
  39. 39.
    Yeo BK, Lu Y (1999) Array failure correction with a genetic algorithm. IEEE Trans Antennas Propag 47(5):823–828CrossRefGoogle Scholar
  40. 40.
    Abdou L, Soltani F (2008) OS-CFAR and CMLD threshold optimization in distributed systems using evolutionary strategies. Signal Image Video Process 2(2):155–167CrossRefGoogle Scholar
  41. 41.
    Ouaarab A, Ahiod B, Yang XS (2014) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669CrossRefGoogle Scholar
  42. 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 63–84CrossRefGoogle Scholar
  43. 43.
    Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624MathSciNetCrossRefGoogle Scholar
  44. 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 6Google Scholar
  45. 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):1300–1310CrossRefGoogle Scholar
  46. 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):46–63Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Energy Systems Modeling Laboratory, Electrical Engineering DepartmentUniversity of BiskraBiskraAlgeria
  2. 2.Identification, Command, Control and Communication Laboratory, Electrical Engineering DepartmentUniversity of BiskraBiskraAlgeria

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