Application of cuckoo search algorithm to constrained control problem of a parallel robot platform
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.
KeywordsParallel robot Constrained optimization Cascade control Controller tuning Cuckoo search algorithm
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- 7.Filipovic V, Nedic N (2008) PID regulators. Faculty of Mechanical Engineering, Kraljevo, SerbiaGoogle Scholar
- 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
- 23.Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceeding of IEEE International Conference on NeuralNetworks 4:1942–1948Google Scholar
- 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.Goldberg DE (1989) Genetic algorithms in search, optimisation and machine learning, reading, mass.: Addison WesleyGoogle Scholar
- 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
- 34.Jelali M, Kroll A (2002) Hydraulic servo-systems. Springer, BerlinGoogle Scholar
- 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.Yang XS (2014) Nature-inspired optimization algorithms. ElsevierGoogle Scholar