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A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform

Author

Listed:
  • Vladimir Stojanovic

    (University of Kragujevac)

  • Novak Nedic

    (University of Kragujevac)

Abstract

This paper presents the optimal tuning of cascade load force controllers for a parallel robot platform. A parameter search for the proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. The proposed parameter search scheme is based on a bat algorithm, which attracts a lot of attention in the evolutionary computation area due to the empirical evidence of its superiority in solving various nonconvex problems. 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 simulation results indicate that the proposed optimal tuned cascade control is effective and efficient. These results clearly demonstrate that applied techniques exhibit a significant performance improvement over classical tuning methods.

Suggested Citation

  • Vladimir Stojanovic & Novak Nedic, 2016. "A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 332-347, January.
    • Handle: RePEc:spr:joptap:v:168:y:2016:i:1:d:10.1007_s10957-015-0706-z
    DOI: 10.1007/s10957-015-0706-z
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    References listed on IDEAS

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    1. Abdelmadjid Recioui, 2012. "Sidelobe Level Reduction in Linear Array Pattern Synthesis Using Particle Swarm Optimization," Journal of Optimization Theory and Applications, Springer, vol. 153(2), pages 497-512, May.
    2. Y. L. Zheng & S. L. Nie & H. Ji & Z. Hu, 2013. "Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty," Journal of Optimization Theory and Applications, Springer, vol. 157(1), pages 276-286, April.
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    Cited by:

    1. Lu Liu & Siyuan Tian & Dingyu Xue & Tao Zhang & YangQuan Chen, 2019. "Industrial feedforward control technology: a review," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2819-2833, December.
    2. Xiaofeng Wang & Shu Guo & Jian Shen & Yang Liu, 2020. "Optimization of preventive maintenance for series manufacturing system by differential evolution algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 745-757, March.
    3. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.
    4. Zhonglei Liu & Xuekun Li & Dingzhu Wu & Zhiqiang Qian & Pingfa Feng & Yiming Rong, 2019. "The development of a hybrid firefly algorithm for multi-pass grinding process optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2457-2472, August.

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