Application of cuckoo search algorithm to constrained control problem of a parallel robot platform

  • Vladimir StojanovicEmail author
  • Novak Nedic
  • Dragan Prsic
  • Ljubisa Dubonjic
  • Vladimir Djordjevic


This paper presents a cascade load force control design for a parallel robot platform. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on cuckoo search (CS) is suggested to effectively search parameters of the cascade controllers. The control design problem is formulated as an optimization problem under constraints. Typical constraints, such as mechanical limits on positions and maximal velocities of hydraulic actuators as well as on servo-valve positions, are included in the proposed algorithm. The optimal results are compared to the state-of-the-art algorithms for these problem instances (NP-hard and constrained optimization problems). Simulation results also show that applied optimal tuned cascade control algorithm exhibits a significant performance improvement over classical tuning methods.


Parallel robot Constrained optimization Cascade control Controller tuning Cuckoo search algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Stewart D (1965) A platform with six degrees of freedom. Proc Inst Mech Eng 180(15):371–386CrossRefGoogle Scholar
  2. 2.
    Pi Y, Wang X (2011) Trajectory tracking control of a 6-DOF hydraulic parallel robot manipulator with uncertain load disturbances. Control Eng Pract 19(2):185–193CrossRefGoogle Scholar
  3. 3.
    Stefanovic M, Zhang H (2012) Results on the robust observer-based position controller for parallel kinematic machines. J Intell Robot Syst 66:417–428CrossRefzbMATHGoogle Scholar
  4. 4.
    Su YX, Duan DY, Zheng CH (2004) Nonlinear PID control of a six-DOF parallel manipulator. Control Theory and Applications, IEE Proceedings 151(1):95–102CrossRefGoogle Scholar
  5. 5.
    Dihovicni D, Nedic N (2008) Simulation, animation and program support for a high performance pneumatic force actuator system. Math Comput Model 48(5–6):761–768CrossRefGoogle Scholar
  6. 6.
    Filipovic V, Nedic N, Stojanovic V (2011) Robust identification of pneumatic servo actuators in the real situations. Forsch Ingenieurwes 75(4):183–196CrossRefGoogle Scholar
  7. 7.
    Filipovic V, Nedic N (2008) PID regulators. Faculty of Mechanical Engineering, Kraljevo, SerbiaGoogle Scholar
  8. 8.
    Guo HB, Liu YG, Liu GR, Li HR (2008) Cascade control of a hydraulically driven 6-DOF parallel robot manipulator based on a sliding mode. Control Eng Pract 16(9):1055–1068CrossRefGoogle Scholar
  9. 9.
    Heintze J, Teerhuis PC, Van der Weiden AJJ (1996) Controlled hydraulics for a direct drive brick laying robot. Autom Constr 5:23–29CrossRefGoogle Scholar
  10. 10.
    Sepehri N, Dumont GAM, Lawrence PD, Sassani F (1990) Cascade control of hydraulically actuated manipulators. Robotica 8:207–216CrossRefGoogle Scholar
  11. 11.
    Rao SS (2009) Engineering optimization: theory and practice. Wiley, New YorkCrossRefGoogle Scholar
  12. 12.
    Doncieux S, Bredeche N, Mouret JB (2011) New horizons in evolutionary robotics: extended contributions from the 2009 EvoDeRob workshop (studies in computational intelligence). Springer -Verlag, Berlin HeidelbergCrossRefGoogle Scholar
  13. 13.
    Chen CT (2012) Reconfiguration of a parallel kinematic manipulator for the maximum dynamic load-carrying capacity. Mech Mach Theory 54:62–75CrossRefGoogle Scholar
  14. 14.
    Geng L, Liu PL, Liu K (2015) Optimization of cutter posture based on cutting force prediction for five-axis machining with ball-end cutters. Int J Adv Manuf Technol. doi: 10.1007/s00170-014-6719-1 Google Scholar
  15. 15.
    Kucuk S (2013) Energy minimization for 3-RRR fully planar parallel manipulator using particle swarm optimization. Mech Mach Theory 62:129–149CrossRefGoogle Scholar
  16. 16.
    Yu M, Zhang Y, Chen K, Zhang D (2014) Integration of process planning and scheduling using a hybrid GA/PSO algorithm. Int J Adv Manuf Technol. doi: 10.1007/s00170-014-6669-7 Google Scholar
  17. 17.
    Lou Y, Zhang Y, Huang R, Chen X, Li Z (2014) Optimization algorithms for kinematically optimal design of parallel manipulators. IEEE Trans Autom Sci Eng 11(2):574–584CrossRefGoogle Scholar
  18. 18.
    Saputra VB, Ong SK, Nee AY (2010) A PSO algorithm for mapping the workspace boundary of parallel manipulators. IEEE International Conference on Robotics and Automation. 4691 – 4696Google Scholar
  19. 19.
    Mesloub H, Benchouia MT, Goléa A, Goléa N, Benbouzid MEH (2016) Predictive DTC schemes with PI regulator and particle swarm optimization for PMSM drive: comparative simulation and experimental study. Int J Adv Manuf Technol. doi: 10.1007/s00170-016-8406-x Google Scholar
  20. 20.
    Glattfelder AH, Schaufelberger W (2013) Control systems with input and output constraints (advanced textbooks in control and signal processing). Springer -Verlag, LondonzbMATHGoogle Scholar
  21. 21.
    Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Proceedings of world congress on nature & biologically inspired computing, December 2009, India. IEEE Publications, USA, pp 210–214CrossRefGoogle Scholar
  22. 22.
    Yang XS, Deb S (2010) Engineering optimisation by Cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 2(4):330–343CrossRefzbMATHGoogle Scholar
  23. 23.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceeding of IEEE International Conference on NeuralNetworks 4:1942–1948Google Scholar
  24. 24.
    Shi Y, Eberhart RC (1998) In: Porto VW, Saravanan N, Waagen D, Eibe A (eds) Parameter selection in particle swarm optimization, proceedings of the seventh annual conference on evolutionary programming. Springer-Verlag, Berlin, Germany, pp 591–600Google Scholar
  25. 25.
    Goldberg DE (1989) Genetic algorithms in search, optimisation and machine learning, reading, mass.: Addison WesleyGoogle Scholar
  26. 26.
    Rao SS, Pan TS, Venkayya VB (1991) Optimal placement of actuators in actively controlled structures using genetic algorithms. AIAA J 29(6):942–943CrossRefGoogle Scholar
  27. 27.
    Mitchell M (1998) An introduction to genetic algorithms. MIT Press, CambridgezbMATHGoogle Scholar
  28. 28.
    Rao SS, Pan TS, Dhingra AK, Venkayya VB, Kumar V (1990) Genetic evolution-based optimization methods for engineering design. Proceedings of the 3rd Air Force/NASA Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, San Francisco, pp 318–323Google Scholar
  29. 29.
    Hajela P (1990) Genetic search: an approach to the nonconvex optimization problem. AIAA J 26(7):1205–1210CrossRefGoogle Scholar
  30. 30.
    Lin CY, Hajela P (1992) Genetic algorithms in optimization problems with discrete and integer design variables. Eng Optim 19:309–327CrossRefGoogle Scholar
  31. 31.
    Zalzala AMS, Fleming PJ (1997) Genetic algorithms in engineering systems. The Institution of Electrical Engineers (IEE), LondonCrossRefzbMATHGoogle Scholar
  32. 32.
    Nasiri MM (2013) A pseudo particle swarm optimization for the RCPSP. Int J Adv Manuf Technol 65:909–918CrossRefGoogle Scholar
  33. 33.
    Li X, Gao L, Wen X (2013) Application of an efficient modified particle swarm optimization algorithm for process planning. Int J Adv Manuf Technol 67:1355–1369CrossRefGoogle Scholar
  34. 34.
    Jelali M, Kroll A (2002) Hydraulic servo-systems. Springer, BerlinGoogle Scholar
  35. 35.
    Pavlyukevich I (2007) Lévy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Cominos P, Munro N (2002) PID controllers: recent tuning methods and design to specification. Control Theory and Applications, IEEE Proceedings 149(1):46–53CrossRefGoogle Scholar
  37. 37.
    Omran A, Kassem A (2011) Optimal task space control design of a Stewart manipulator for aircraft stall recovery. Aerosp Sci Technol 15:353–365CrossRefGoogle Scholar
  38. 38.
    Omran A, Kassem A, El-Bayoumi G, Bayoumi M (2009) Mission-based optimal control of Stewart manipulator. Aircraft Engineering & Aerospace Technology Journal 81(3):147–153Google Scholar
  39. 39.
    Yang XS (2014) Nature-inspired optimization algorithms. ElsevierGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Vladimir Stojanovic
    • 1
    Email author
  • Novak Nedic
    • 1
  • Dragan Prsic
    • 1
  • Ljubisa Dubonjic
    • 1
  • Vladimir Djordjevic
    • 1
  1. 1.Faculty of Mechanical and Civil Engineering in Kraljevo, Department of Automatic Control, Robotics and Fluid TechniqueUniversity of KragujevacKraljevoSerbia

Personalised recommendations