Arabian Journal for Science and Engineering

, Volume 42, Issue 2, pp 739–750 | Cite as

Optimum Design of Fractional-Order Hybrid Fuzzy Logic Controller for a Robotic Manipulator

Research Article - Computer Engineering and Computer Science


The robotic manipulators are complex and coupled nonlinear systems. Therefore, the designing of an effective controller for these systems is quite complicated. The main hurdle in operating these systems is the inter-linkage between the links, and this can be removed by using any decoupling method. The decoupling between the links is not a good idea from the viewpoint of practical applications. In this paper, a fractional-order hybrid fuzzy logic controller (FOHFLC) scheme is developed for a two-degree-of-freedom rigid planar robotic manipulator with payload (2-DOF RPRMWP) plant for the trajectory tracking. The cuckoo search algorithm (CSA) is utilized for finding the optimal parameters of the proposed approach. For witnessing the effectiveness, the performance of proposed FOHFLC scheme is compared with integer-order hybrid FLC (IOHFLC) approach and conventional PID controller. The robustness testing is investigated for parameter variations and disturbance rejection for the proposed controller schemes.


Robotic manipulator Fuzzy logic controller Fractional-order operators Cuckoo search algorithm Trajectory tracking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lian, R.-J.; Lin, B.-F.: Design of a mixed fuzzy controller for multiple-input multiple-output systems. Mechatronics 15, 1225–1252 (2005)CrossRefGoogle Scholar
  2. 2.
    Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller-part 1. IEEE Trans. Syst. Man Cybern. 20(2), 404–418 (1990)CrossRefMATHGoogle Scholar
  3. 3.
    Ohtani, Y.; Yoshimura, T.: Fuzzy control of a manipulator using the concept of sliding mode. Int. J. Syst. Sci. 27(2), 179–186 (1996)CrossRefMATHGoogle Scholar
  4. 4.
    Hazzab, A.; Bousserhane, I.K.; Zerbo, M.; Sicard, P.: Real-time implementation of fuzzy gain scheduling of PI controller for induction motor machine control. Neural Process. Lett. 24, 203–215 (2005)CrossRefGoogle Scholar
  5. 5.
    Xu, C.; Shin, Y.C.: A multilevel fuzzy control design for a class of multi input single-output systems. IEEE Trans. Ind. Electron. 59(8), 3113–3123 (2012)CrossRefGoogle Scholar
  6. 6.
    Huo, B.; Li, Y.; Tong, S.: Fuzzy adaptive fault-tolerant output feedback control of multi-input and multi-output non-linear systems in strict-feedback form. IET Control Theory Appl. 6(17), 2704–2715 (2012)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Tong, S.; Sui, S.; Li, Y.: Fuzzy adaptive output feedback control of MIMO nonlinear systems with partial tracking errors constrained. IEEE Trans. Fuzzy Syst. 23(4), 729–742 (2015)CrossRefGoogle Scholar
  8. 8.
    Yadav, A.K.; Gaur, P.: An optimized and improved STF-PID speed control of throttle controlled HEV. Arab. J. Sci. Eng. 41(9), 1–12 (2016)CrossRefGoogle Scholar
  9. 9.
    Su, Y.; Xu, L.; Li, D.: Adaptive fuzzy control of a class of MIMO nonlinear system with actuator saturation for greenhouse climate control problem. IEEE Trans. Autom. Sci. Eng. 13(2), 772–788 (2016)CrossRefGoogle Scholar
  10. 10.
    Song, Z.; Yi, J.; Zhao, D.; Li, X.: A computed torque controller for uncertain robotic manipulator systems: fuzzy approach. Fuzzy Sets Syst. 154, 208–226 (2005)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Meza, J.L.; Santianez, V.; Soto, R.; Llama, M.A.: Fuzzy self-tuning PID semiglobal regulator for robotic manipulators. IEEE Trans. Ind. Electron. 59(6), 2709–2717 (2012)CrossRefGoogle Scholar
  12. 12.
    Chu, Z.Y.; Cui, J.; Sun, F.: Fuzzy adaptive disturbance-observer based robust tracking control of electrically driven free-floating space manipulator. IEEE Syst. J. 8(2), 343–351 (2014)CrossRefGoogle Scholar
  13. 13.
    Chiu, C.-S.: Mixed feedforward/feedback based adaptive fuzzy control for a class of MIMO nonlniear systems. IEEE Trans. Fuzzy Syst. 14(6), 716–727 (2006)CrossRefGoogle Scholar
  14. 14.
    Baghli, F.Z.; Bakkali, L.E.; Lakhal, Y.: Multi-input multi-output fuzzy logic controller for complex system: application on two-links manipulator. Proc. Technol. 19, 607–614 (2015)CrossRefGoogle Scholar
  15. 15.
    Das, S.; Pan, I.; Das, S.; Gupta, A.: A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices. Eng. Appl. Artif. Intell. 25, 430–442 (2012)CrossRefGoogle Scholar
  16. 16.
    Das, S.; Pan, I.; Das, S.: Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time. ISA Trans. 52, 550–566 (2013)CrossRefGoogle Scholar
  17. 17.
    Sharma, R.; Rana, K.P.S.: Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator. Expert Syst. Appl. 41, 11335–11346 (2014)Google Scholar
  18. 18.
    Hajiloo, A.; Xie, W.-F.: Fuzzy fractional-order PID controller design using multi-objective optimization. In: Proceedings of IFSA World Congress and NAFIPS Annual meeting 2013 Joint, Edmonton, 24–28 June 2013Google Scholar
  19. 19.
    Lin, F.: Robust Control Design: An Optimal Control Approach. Wiley, Chichester (2007)CrossRefGoogle Scholar
  20. 20.
    Oustaloup, A.; Levron, F.; Mathieu, B.; Nanot, F.M.: Frequency-band complex noninteger differentiator: characterization and synthesis. IEEE Trans. Circuits Syst. I: Fundam. Theory Appl. 47(1), 25–39 (2000)CrossRefGoogle Scholar
  21. 21.
    Tang, Y.; Cui, M.; Hua, C.; Li, L.; Yang, Y.: Optimum design of fractional order \(\text{ PI }^{\lambda }\text{ D }^{\mu }\) controller for AVR system using chaotic ant swarm. Expert Syst. Appl. 39, 6887–6896 (2012)CrossRefGoogle Scholar
  22. 22.
    Pan, I.; Das, S.: Chaotic multi-objective optimization based design of fractional order \(\text{ PI }^{\lambda }\text{ D }^{\mu }\) controller in AVR system. Electr. Power Energy Syst. 43, 393–407 (2012)CrossRefGoogle Scholar
  23. 23.
    Yang, X.S.; Deb, S.: Cuckoo Search via Lévy flights. In: Proceedings World Congress on Nature and Biologically Inspired Computing India, pp. 210–214 (2009)Google Scholar
  24. 24.
    Gandomi, A.H.; Yang, X.-S.; Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013)CrossRefGoogle Scholar
  25. 25.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11, 5508–5518 (2011)CrossRefGoogle Scholar
  26. 26.
    Yang, X.-S.; Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1(4), 330–343 (2010)MATHGoogle Scholar
  27. 27.
    Tan, W.S.; Hassan, M.Y.; Majid, M.S.; Rahman, H.A.: Allocation and Sizing of DG using cuckoo search algorithm. In: 2012 IEEE International Conference on Power and Energy (PEcon) Kota Kinabalu Sabah, Malaysia, pp. 133–8 (2012)Google Scholar
  28. 28.
    Yildiz, A.R.: Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int. J. Adv. Manuf. Technol. 64(1–4), 55–61 (2013)CrossRefGoogle Scholar
  29. 29.
    Bulatovic, R.R.; Dordevic, S.R.; Dordevic, V.S.: Cuckoo search algorithm: a metaheuristic approach to solving the problem of optimum synthesis of a six-bar double dwell linkage. Mech. Mach. Theory 61, 1–13 (2013)CrossRefGoogle Scholar
  30. 30.
    Ayala, H.V.H.; Coelho, L.D.S.: Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Syst. Appl. 39, 8968–8974 (2012)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2016

Authors and Affiliations

  1. 1.Electrical and Instrumentation Engineering DepartmentThapar UniversityPatialaIndia
  2. 2.Instrumentation and Control Engineering DivisionNetaji Subhas Institute of TechnologyDwarkaIndia

Personalised recommendations