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The mechanical arm control based on harmony search genetic algorithm

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Considering the low efficiency and instability of traditional genetic algorithm optimized PID controller, an improved algorithm named harmony search genetic algorithm to optimize PID controller’s parameters of mechanical arm is proposed in this paper. Using harmony search algorithm in the initial population generation process of genetic algorithm improved the algorithm’s performance. Harmony search genetic algorithm is more suitable to optimize PID controller’s parameters than traditional genetic algorithm in six degrees of freedom mechanical arm system. Compared to the traditional control optimization method, as shown in the simulation results, the new kind of optimization method is better in both validity and stability.

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  1. 1.

    Xu, R.R., Yao, L.: Robot nonsingular terminal synovial control based on genetic algorithm. J. Qingdao Univ. Sci. Technol. 35(4), 422–427 (2014)

  2. 2.

    Yoshiyuki, T., Naoki, Y., Toshio, T., Takamasa, S.: Vehicle active steering control system based on human mechanical impedance properties of the arms. IEEE Trans. Intell. Transp. Syst. 15(4), 1758–1769 (2014)

  3. 3.

    Serra Ginalber, L.O., Silva, J.A.: Robust PID TS fuzzy control methodology based on gain and phase margins specifications. J. Intell. Fuzzy Syst. 26(2), 869–888 (2014)

  4. 4.

    Tumari, M.Z.M., Ahmad, M.A., Saealal, M.S., Zawawi, M.A., Mohamed, Z., Yusop, N.M. The direct strain feedback with PID control approach for a flexible manipulator: experimental results. In: International Conference on Control, Automation and Systems, pp. 7–12 (2011)

  5. 5.

    Chang, T.K., Spowage, A., Chan, K.Y.: Review of control and sensor system of flexible manipulator. J. Intell. Robot. Syst. 77(1), 187–213 (2015)

  6. 6.

    Lee, C.S., Gonzalez, R.V.: Fuzzy logic versus a PID controller for position control of a muscle-like actuated arm. J. Mech. Sci. Technol. 22(8), 1475–1482 (2008)

  7. 7.

    Mahmoodabadi, M.J., Taherkhorsandi, M., Talebipour, M., Castillo-Villar, K.K.: Adaptive robust PID control subject to supervisory decoupled sliding mode control based upon genetic algorithm optimization. Trans. Inst. Meas. Control 37(4), 505–514 (2015)

  8. 8.

    Neath, M.J., Swain, A.K., Madawala, U.K., Thrimawithana, D.J.: An optimal PID controller for a bidirectional inductive power transfer system using multiobjective genetic algorithm. IEEE Trans. Power Electron. 29(3), 1523–1531 (2014)

  9. 9.

    Katal, N., Singh, S.K.: Multi-objective optimisation of PID controller for DC servo motor using genetic algorithm. Eng. Intell. Syst. 23(1), 7–16 (2015)

  10. 10.

    Chen, Y., Ma, Y., Yun, W.: Application of improved genetic algorithm in PID controller parameters optimization. Telkomnika Indonesian J. Electr. Eng. 11(3), 1524–1530 (2013)

  11. 11.

    Taherkhorsandi, M., Mahmoodabadi, M.J., Talebipour, M., Castillo-Villar, K.K.: Pareto design of an adaptive robust hybrid of PID and sliding control for a biped robot via genetic algorithm optimization. Nonlinear Dyn. 79(1), 251–263 (2015)

  12. 12.

    Ambia, M.N., Hasanien, H.M., Al-Durra, A., Muyeen, S.M.: Harmony search algorithm-based controller parameters optimization for a distributed-generation system. IEEE Trans. Power Deliv. 30(1), 246–255 (2015)

  13. 13.

    Wang, G., Gandomi, A.H., Zhao, X., Chu, H.: Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft. Comput. 20(1), 273–285 (2016)

  14. 14.

    Upadhyay, P., Kar, R., Mandal, D., Ghoshal, S.P., Mukherjee, V.: A novel design method for optimal IIR system identification using opposition based harmony search algorithm. J. Franklin Inst. 351(5), 2454–2488 (2014)

  15. 15.

    Alia, O.M., Mandava, R.: The variants of the harmony search algorithm: an overview. Artif. Intell. Rev. 36(1), 49–68 (2011)

  16. 16.

    Rafe, V., Paiandeh, Z., Nikanjam, A.: A hybrid optimization algorithm based on harmony search and differential evolution for continuous domain. J. Intell. Fuzzy Syst. 29(5), 2169–2176 (2015)

  17. 17.

    Mahamood, R.M.: Adaptive Controller Design for Two-Link Flexible Manipulator. Springer, Berlin (2014)

  18. 18.

    Das, S.K., Amitava, C., Rakshit, A.: Harmony search-based hybrid stable adaptive fuzzy tracking controllers for vision-based mobile robot navigation. Mach. Vis. Appl. 25(2), 405–419 (2014)

  19. 19.

    Nadi, F., Khader, A.T., Al-Betar, M.A.: Adaptive genetic algorithm using harmony search. In: Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, pp. 819–820 (2010)

  20. 20.

    Talebpour, M.H., Kaveh, A., Kalatjari, V.R.: Optimization of skeletal structures using a hybridized ant colony-harmony search-genetic algorithm. Iran. J. Sci. Technol. Trans. Civil Eng. 38(C1), 1–20 (2014)

  21. 21.

    Ko, K.E., Park, S.M., Park, J., et al.: Training HMM structure and parameters with genetic algorithm and harmony search algorithm. J. Electr. Eng. Technol. 7(7), 109–114 (2012)

  22. 22.

    Shi, W.W., Han, W., Si, W.C.: A hybrid genetic algorithm based on harmony search and its improving. Inform. Manag. Sci. I, 101–109 (2013)

  23. 23.

    Mao, C.: Harmony search-based test data generation for branch coverage in software structural testing. Neural Comput. Appl. 25(1), 199–216 (2014)

  24. 24.

    Ho, M.T., Tu, Y.W.: Position control of a single-link flexible manipulator using H\(\infty \)-based PID control. IEE Proc. Control Theory Appl. 153(5), 615–622 (2006)

  25. 25.

    Ligang, W., Xiaozhan, Y., Hak-Keung, L.: Dissipativity analysis and synthesis for discrete-time t-s fuzzy stochastic system swith time-varying delay. IEEE Trans. Fuzzy Syst. 22(2), 380–394 (2014)

  26. 26.

    Hu, X., Wu, L., Hu, C., Wang, Z., Gao, H.: Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T-S fuzzy approach. Int. J. Syst. Sci. 45(8), 1740–1756 (2014)

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This paper is supported by State Key Laboratory of Robotics and Systems (HIT).

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Correspondence to Zhaolan He.

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He, Z., Pan, B., Liu, Z. et al. The mechanical arm control based on harmony search genetic algorithm. Cluster Comput 20, 3251–3261 (2017).

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  • Harmony search algorithm
  • Genetic algorithm
  • Mechanical arm
  • PID control