Skip to main content
SpringerLink
Log in

An Effective Controller Design Approach for Magnetic Levitation System Using Novel Improved Manta Ray Foraging Optimization

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This paper demonstrates the development of a novel metaheuristic algorithm which has an enhanced diversification and intensification features and aims to construct an efficient mechanism via the developed algorithm to control a magnetic object suspension. Manta ray foraging optimization (MRFO) algorithm together with generalized opposition-based learning (GOBL) technique and Nelder–Mead (NM) simplex search method is used to define the general frame of the developed algorithm. The developed novel algorithm (Ob-MRFONM) employs the NM method for better intensification whereas integrates the GOBL technique for better diversification. The constructed Ob-MRFONM algorithm is confirmed to have enhanced capabilities via evaluations against well-known unimodal and multimodal benchmark functions. The developed algorithm is also utilized to reach optimum values of a real PID plus second-order derivative (PIDD2) controller employed in a magnetic object suspension system to demonstrate the capability of the Ob-MRFONM algorithm for such a complex real-world engineering problem. It is worth noting that this paper is the first report in the literature that demonstrates the application of a real PIDD2 controller in a magnetic object suspension system. To reach better capability, a new objective function is used for minimization. Then, the proposed approach is comparatively evaluated in terms of statistical and nonparametric statistical analysis, convergence, transient response, disturbance rejection, controller effort and effects of the noise signal. The demonstrated results also confirm the highly competitive capability of the proposed algorithm for the complex magnetic object suspension system. The nonlinear model of the system is also used in this study in order to validate the linearized model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Yadav, S.; Verma, S.K.; Nagar, S.K.: Optimized PID Controller for magnetic levitation system. IFAC-PapersOnline 49, 778–782 (2016). https://doi.org/10.1016/j.ifacol.2016.03.151

    Article  Google Scholar 

  2. García-Gutiérrez, G.; Arcos-Aviles, D.; Carrera, E.V.; Guinjoan, F.; Motoasca, E.; Ayala, P.; Ibarra, A.: Fuzzy logic controller parameter optimization using metaheuristic cuckoo search algorithm for a magnetic levitation system. Appl. Sci. 9, 2458 (2019). https://doi.org/10.3390/app9122458

    Article  Google Scholar 

  3. Demirören, A.; Ekinci, S.; Hekimoğlu, B.; Izci, D.: Opposition-based artificial electric field algorithm and its application to FOPID controller design for unstable magnetic ball suspension system. Eng. Sci. Technol. Int. J. 24, 469–479 (2021). https://doi.org/10.1016/j.jestch.2020.08.001

    Article  Google Scholar 

  4. Sadek, U.; Sarjaš, A.; Chowdhury, A.; Svečko, R.: Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search. Appl. Soft Comput. 56, 19–33 (2017). https://doi.org/10.1016/j.asoc.2017.02.032

    Article  Google Scholar 

  5. Yadav, S.; Verma, S.K.; Nagar, S.K.: Performance enhancement of magnetic levitation system using teaching learning based optimization. Alexandria Eng. J. 57, 2427–2433 (2018). https://doi.org/10.1016/j.aej.2017.08.016

    Article  Google Scholar 

  6. Mughees, A.; Mohsin, S.A.: Design and control of magnetic levitation system by optimizing fractional order pid controller using ant colony optimization algorithm. IEEE Access. 8, 116704–116723 (2020). https://doi.org/10.1109/ACCESS.2020.3004025

    Article  Google Scholar 

  7. Verma, S.K.; Yadav, S.; Nagar, S.K.: Optimization of fractional order PID controller using grey wolf optimizer. J. Control Autom. Electr. Syst. 28, 314–322 (2017). https://doi.org/10.1007/s40313-017-0305-3

    Article  Google Scholar 

  8. Pradhan, R.; Majhi, S.K.; Pradhan, J.K.; Pati, B.B.: Optimal fractional order PID controller design using ant lion optimizer. Ain Shams Eng. J. 11, 281–291 (2020). https://doi.org/10.1016/j.asej.2019.10.005

    Article  Google Scholar 

  9. Sahib, M.A.: A novel optimal PID plus second order derivative controller for AVR system. Eng. Sci. Technol. Int. J. 18, 194–206 (2015). https://doi.org/10.1016/j.jestch.2014.11.006

    Article  Google Scholar 

  10. Seul, J.; Dorf, R.C.: Analytic PIDA controller design technique for a third order system. In: Proceedings of 35th IEEE Conference on Decision and Control. pp. 2513–2518. IEEE (1996)

  11. Kumar, M.; Hote, Y.V.: Real-time performance analysis of PIDD2 controller for nonlinear twin rotor TITO aerodynamical system. J. Intell. Robot. Syst. 101, 55 (2021). https://doi.org/10.1007/s10846-021-01322-4

    Article  Google Scholar 

  12. Raju, M.; Saikia, L.C.; Sinha, N.: Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. Int. J. Electr. Power Energy Syst. 80, 52–63 (2016). https://doi.org/10.1016/j.ijepes.2016.01.037

    Article  Google Scholar 

  13. Kumar, M.; Hote, Y.V.: Robust PIDD2 controller design for perturbed load frequency control of an interconnected time-delayed power systems. IEEE Trans. Control Syst. Technol. 29, 2662 (2020). https://doi.org/10.1109/TCST.2020.3043447

    Article  Google Scholar 

  14. Mokeddem, D.; Mirjalili, S.: Improved whale optimization algorithm applied to design PID plus second-order derivative controller for automatic voltage regulator system. J. Chin. Inst. Eng. 43, 541–552 (2020). https://doi.org/10.1080/02533839.2020.1771205

    Article  Google Scholar 

  15. Kumar, M.; Hote, Y.V.: Maximum sensitivity-constrained coefficient diagram method-based PIDA controller design: application for load frequency control of an isolated microgrid. Electr. Eng. (2021). https://doi.org/10.1007/s00202-021-01226-4

    Article  Google Scholar 

  16. Jaradat, M.A.; Sawaqed, L.S.; Alzgool, M.M.: Optimization of PIDD2-FLC for blood glucose level using particle swarm optimization with linearly decreasing weight. Biomed. Signal Process. Control 59, 101922 (2020). https://doi.org/10.1016/j.bspc.2020.101922

    Article  Google Scholar 

  17. Ahmad, I.; Shahzad, M.; Palensky, P.: Optimal PID control of magnetic levitation system using genetic algorithm. In: 2014 IEEE International Energy Conference (ENERGYCON). pp. 1429–1433. IEEE (2014)

  18. Ozaydin, C.; Zeynelgil, H.L.; Ekinci, S.; Hekimoglu, B.: PID controller design based on sine cosine algorithm for magnetic ball suspension system. In: 2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019. pp. 1–7 (2019)

  19. Bauer, W.; Baranowski, J.: Fractional PIλD controller design for a magnetic levitation system. Electronics 9, 2135 (2020). https://doi.org/10.3390/electronics9122135

    Article  Google Scholar 

  20. Ekinci, S.; Demiroren, A.; Hekimoglu, B.; Eker, E.: Performance enhancement of magnetic ball suspension system using hybrid whale optimization algorithm with simulated annealing. In: 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings. pp. 1–6. IEEE (2019)

  21. Ataşlar-Ayyıldız, B.; Karahan, O.: Design of a MAGLEV system with PID based fuzzy control using CS algorithm. Cybern. Inf. Technol. 20, 5–19 (2020). https://doi.org/10.2478/cait-2020-0037

    Article  Google Scholar 

  22. Abbas, N.H.: Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm. Int. J. Electr. Comput. Eng. 10, 2402–2415 (2019). https://doi.org/10.11591/ijece.v10i3.pp2402-2415

    Article  Google Scholar 

  23. Varshney, A., Bhushan, B.: Trajectory tracking and ball position control of magnetic levitation system using swarm intelligence technique. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). pp. 29–35 (2020)

  24. Roy, P.; Borah, M.; Majhi, L.; Singh, N.: Design and implementation of FOPID controllers by PSO, GSA and PSOGSA for MagLev system. In: 2015 International Symposium on Advanced Computing and Communication, ISACC 2015. pp. 10–15 (2016)

  25. Zhao, W.; Zhang, Z.; Wang, L.: Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng. Appl. Artif. Intell. 87, 103300 (2020). https://doi.org/10.1016/j.engappai.2019.103300

    Article  Google Scholar 

  26. El-Hameed, M.A.; Elkholy, M.M.; El-Fergany, A.A.: Three-diode model for characterization of industrial solar generating units using manta-rays foraging optimizer: analysis and validations. Energy Convers. Manag. 219, 113048 (2020). https://doi.org/10.1016/j.enconman.2020.113048

    Article  Google Scholar 

  27. Shaheen, A.M.; Ginidi, A.R.; El-Sehiemy, R.A.; Ghoneim, S.S.M.: Economic power and heat dispatch in cogeneration energy systems using manta ray foraging optimizer. IEEE Access 8, 208281–208295 (2020). https://doi.org/10.1109/ACCESS.2020.3038740

    Article  Google Scholar 

  28. Fathy, A.; Rezk, H.; Yousri, D.: A robust global MPPT to mitigate partial shading of triple-junction solar cell-based system using manta ray foraging optimization algorithm. Sol. Energy 207, 305–316 (2020). https://doi.org/10.1016/j.solener.2020.06.108

    Article  Google Scholar 

  29. Turgut, O.E.: A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler. SN Appl. Sci. 3, 3 (2020). https://doi.org/10.1007/s42452-020-04013-1

    Article  Google Scholar 

  30. Ghosh, K.K.; Guha, R.; Bera, S.K.; Kumar, N.; Sarkar, R.: S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem. Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-020-05560-9

    Article  Google Scholar 

  31. Izci, D.; Ekinci, S.; Eker, E.; Kayri, M.: Improved manta ray foraging optimization using opposition-based learning for optimization problems. In: 2020 International Congress on Human–Computer Interaction, Optimization and Robotic Applications (HORA). pp. 1–6. IEEE (2020)

  32. Nelder, J.A.; Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965). https://doi.org/10.1093/comjnl/7.4.308

    Article  MathSciNet  MATH  Google Scholar 

  33. Rajan, A.; Malakar, T.: Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm. Int. J. Electr. Power Energy Syst. 66, 9–24 (2015). https://doi.org/10.1016/j.ijepes.2014.10.041

    Article  Google Scholar 

  34. Xu, S.; Wang, Y.; Liu, X.: Parameter estimation for chaotic systems via a hybrid flower pollination algorithm. Neural Comput. Appl. 30, 2607–2623 (2018). https://doi.org/10.1007/s00521-017-2890-2

    Article  Google Scholar 

  35. Xu, S.; Wang, Y.; Wang, Z.: Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder–Mead simplex method. Energy 173, 457–467 (2019). https://doi.org/10.1016/j.energy.2019.02.106

    Article  Google Scholar 

  36. Izci, D.; Ekinci, S.; Orenc, S.; Demiroren, A.: Improved artificial electric field algorithm using Nelder–Mead simplex method for optimization problems. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). pp. 1–5. IEEE (2020)

  37. Abdel-Basset, M.; Mohamed, R.; Mirjalili, S.: A novel whale optimization algorithm integrated with Nelder–Mead simplex for multi-objective optimization problems. Knowledge-Based Syst. 212, 106619 (2021). https://doi.org/10.1016/j.knosys.2020.106619

    Article  Google Scholar 

  38. Wang, H.; Wu, Z.; Rahnamayan, S.; Liu, Y.; Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Inf. Sci. (Ny) 181, 4699–4714 (2011). https://doi.org/10.1016/j.ins.2011.03.016

    Article  MathSciNet  Google Scholar 

  39. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). pp. 695–701. IEEE (2005)

  40. Yu, S.; Zhu, S.; Ma, Y.; Mao, D.: Enhancing firefly algorithm using generalized opposition-based learning. Computing 97, 741–754 (2015). https://doi.org/10.1007/s00607-015-0456-7

    Article  MathSciNet  MATH  Google Scholar 

  41. Jain, P.; Saxena, A.: An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market. Eng. Sci. Technol. Int. J. 22, 1047–1067 (2019). https://doi.org/10.1016/j.jestch.2019.03.005

    Article  Google Scholar 

  42. Hans, R.; Kaur, H.; Kaur, N.: Opposition-based Harris Hawks optimization algorithm for feature selection in breast mass classification. J. Interdiscip. Math. 23, 97–106 (2020). https://doi.org/10.1080/09720502.2020.1721670

    Article  Google Scholar 

  43. Ekinci, S.; Hekimoğlu, B.; Izci, D.: Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor. Eng. Sci. Technol. Int. J. 24, 331–342 (2021). https://doi.org/10.1016/j.jestch.2020.08.011

    Article  Google Scholar 

  44. Zhao, W.; Wang, L.; Zhang, Z.: Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput. Appl. 32, 9383–9425 (2020). https://doi.org/10.1007/s00521-019-04452-x

    Article  Google Scholar 

  45. Houssein, E.H.; Saad, M.R.; Hashim, F.A.; Shaban, H.; Hassaballah, M.: Lévy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng. Appl. Artif. Intell. 94, 103731 (2020). https://doi.org/10.1016/j.engappai.2020.103731

    Article  Google Scholar 

  46. Abualigah, L.; Diabat, A.; Mirjalili, S.; Abd Elaziz, M.; Gandomi, A.H.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021). https://doi.org/10.1016/j.cma.2020.113609

    Article  MathSciNet  MATH  Google Scholar 

  47. Izci, D.; Ekinci, S.: Comparative performance analysis of slime mould algorithm for efficient design of proportional–integral–derivative controller. Electrica 21, 151–159 (2021). https://doi.org/10.5152/electrica.2021.20077

    Article  Google Scholar 

  48. Lagarias, J.C.; Reeds, J.A.; Wright, M.H.; Wright, P.E.: Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J. Optim. 9, 112–147 (1998). https://doi.org/10.1137/S1052623496303470

    Article  MathSciNet  MATH  Google Scholar 

  49. Xu, S.; Wang, Y.: Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers. Manag. 144, 53–68 (2017). https://doi.org/10.1016/j.enconman.2017.04.042

    Article  Google Scholar 

  50. Golnaraghi, F.; Kuo, B.C.: Automatic Control Systems. McGraw-Hill Education, London (2017)

    Google Scholar 

  51. Kumar, M.; Hote, Y.V.: PIDD2 controller design based on internal model control approach for a non-ideal DC–DC boost converter. In: 2021 IEEE Texas Power and Energy Conference (TPEC). pp. 1–6. IEEE (2021)

  52. Izci, D.: Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm. Trans. Inst. Meas. Control. 43, 3195–3211 (2021). https://doi.org/10.1177/01423312211019633

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Batman University, Batman, 72100, Turkey

    Serdar Ekinci

  2. Department of Electronics & Automation, Batman University, Batman, 72060, Turkey

    Davut Izci

  3. Department of Computer and Instructional Technology Education, Van Yuzuncu Yıl University, Van, Turkey

    Murat Kayri

Authors
  1. Serdar Ekinci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davut Izci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Murat Kayri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davut Izci.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ekinci, S., Izci, D. & Kayri, M. An Effective Controller Design Approach for Magnetic Levitation System Using Novel Improved Manta Ray Foraging Optimization. Arab J Sci Eng 47, 9673–9694 (2022). https://doi.org/10.1007/s13369-021-06321-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06321-z

Keywords

Search

Navigation