Skip to main content
SpringerLink
Log in

Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network

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

Abstract

In this paper, the teaching–learning-based optimization-based functional link artificial neural network (FLANN) has been proposed for the real-time identification of Maglev system. This proposed approach has been compared with some of the other state-of-the-art approaches, such as multilayer perceptron–backpropagation, FLANN least mean square, FLANN particle swarm optimization and FLANN black widow optimization. Further, the real-time Maglev system and the identified model are controlled by the Fuzzy PID controller in a closed loop system with proper choice of the controller parameters. The efficacy of the identified model is investigated by comparing the response of both the real-time and identified Fuzzy PID-controlled Maglev system. To validate the dominance of the proposed model, three nonparametric statistical tests, i.e., the sign test, Wilcoxon signed-rank test and Friedman test, are also performed.

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
 Fig. 17
 Fig. 18
 Fig. 19
Fig. 20
 Fig. 21
 Fig. 22
 Fig. 23
 Fig. 24

Similar content being viewed by others

References

  1. Padoan, A.; Astolfi, A.: Nonlinear system identification for autonomous systems via functional equations methods. In: American Control Conference, pp. 1814–1819 (2016)

  2. Subudhi, B.; Ieee, S.M.; Jena, D.: Nonlinear system identification of a twin rotor MIMO system. In: IEEE TENCON, pp. 1–6 (2009)

  3. Weng, B.; Barner, K.E.: Nonlinear system identification in impulsive environments. IEEE Trans. Signal Process. 53, 2588–2594 (2005)

    Article  MathSciNet  Google Scholar 

  4. Forrai, A.: System identification and fault diagnosis of an electromagnetic actuator. IEEE Trans. Control Syst. Technol. 25, 1028–1035 (2017). https://doi.org/10.1109/TCST.2016.2582147

    Article  Google Scholar 

  5. Mondal, A.; Sarkar, P.: A unified approach for identification and control of electro-magnetic levitation system in delta domain. In: International Conference on Control, Instrumentation, Energy and Communication, pp. 314–318 (2016)

  6. Srivatava, S.; Gupta, M.: A novel technique for identification and control of a non linear system. In: International Conference on Computational Intelligence and Networks, pp. s172–176 (2016). https://doi.org/10.1109/CINE.2016.37

  7. Wen, S.; Wang, Y.; Tang, Y.; Xu, Y.; Li, P.; Zhao, T.: Real-time identification of power fluctuations based on LSTM recurrent neural network: a case study on Singapore Power System. IEEE Trans. Ind. Inf. 15, 5266–5275 (2019). https://doi.org/10.1109/tii.2019.2910416

    Article  Google Scholar 

  8. Alqahtani, A.; Marafi, S.; Musallam, B.; El, N.; Abd, D.; Khalek, E.: Photovoltaic Power Forecasting Model Based on Nonlinear System Identification Modèle de prévision de puissance photovoltaïquebasésur l ’. identification de système non-linéaire 39, 243–250 (2016)

    Google Scholar 

  9. Nanda, S.J.; Panda, G.; Majhi, B.: Improved identification of nonlinear dynamic systems using artificial immune system. In: IEEE Conference and Exhibition on Control, Communications and Automation, pp. 268–273 (2008). https://doi.org/10.1109/INDCON.2008.4768838

  10. Patra, J.; Pal, R.; Chatterji, B.N.; Panda, G.: Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans. Syst. 29, 254–262 (1999)

    Google Scholar 

  11. Majhi, B.; Panda, G.: Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization technique. Expert Syst. Appl. 38, 321–333 (2011). https://doi.org/10.1016/j.eswa.2010.06.070

    Article  Google Scholar 

  12. Han, M.: Robust Structure Selection of Radial Basis Function Networks for Nonlinear System Identification (2014)

  13. Chen, W.: Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization, pp. 409–413 (2009). https://doi.org/10.1109/ICNC.2009.233

  14. Kumpati, S.N.; Kannan, P.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1, 4–26 (1990)

    Article  Google Scholar 

  15. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  16. Mallikarjuna, B.; Viswanathan, R.; Naib, B.B.: Feedback-based gait identification using deep neural network classification. J. Crit. Rev. 7, 661–667 (2020). https://doi.org/10.31838/jcr.07.04.125

    Article  Google Scholar 

  17. Vora, D.R.; Rajamani, K.: A hybrid classification model for prediction of academic performance of students: a big data application. Evol. Intell. (2019). https://doi.org/10.1007/s12065-019-00303-9

    Article  Google Scholar 

  18. Guo, Y.; Wang, F.; Lo, J.T.H.: Nonlinear system identification based on recurrent neural networks with shared and specialized memories. In: Asian Control Conference 2018-January, pp. 2054–2059 (2018). https://doi.org/10.1109/ASCC.2017.8287491

  19. Wang, Z.; Gu, H.: Nonlinear system identification based on genetic algorithm and grey function. In: IEEE International Conference on Automation and Logistics, pp. 1741–1744 (2007)

  20. Guoqiang, Y.; Weiguang, L.; Hao, W.: Study of RBF neural network based on PSO algorithm in nonlinear system (2015). https://doi.org/10.1109/ICICTA.2015.217

  21. Kang, D.; Lee, B.; Won, S.: Nonlinear system identification using ARX and SVM with advanced PSO. In: IEEE Industrial Electronics Society, pp. 598–603 (2007)

  22. Panda, G.; Mohanty, D.; Majhi, B.; Sahoo, G.: Identification of nonlinear systems using particle swarm optimization technique. In: IEEE Congress on Evolutionary Computation, pp. 3253–3257 (2007). https://doi.org/10.1109/CEC.2007.4424889

  23. Hayyolalam, V.; PourhajiKazem, A.A.: Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020). https://doi.org/10.1016/j.engappai.2019.103249

    Article  Google Scholar 

  24. Kumar, M.; Kulkarni, A.J.; Satapathy, S.C.: Socio evolution & learning optimization algorithm: a socio-inspired optimization methodology. Future Gener. Comput. Syst. 81, 252–272 (2018). https://doi.org/10.1016/j.future.2017.10.052

    Article  Google Scholar 

  25. Gholizadeh, S.; Milany, A.: An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures. Eng. Optim. 50, 1829–1849 (2018). https://doi.org/10.1080/0305215X.2017.1417402

    Article  MathSciNet  Google Scholar 

  26. Gholizadeh, S.; Ebadijalal, M.: Performance based discrete topology optimization of steel braced frames by a new metaheuristic. Adv. Eng. Softw. 123, 77–92 (2018). https://doi.org/10.1016/j.advengsoft.2018.06.002

    Article  Google Scholar 

  27. Gholizadeh, S.; Danesh, M.; Gheyratmand, C.: A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames. Comput. Struct. 234, 106250 (2020). https://doi.org/10.1016/j.compstruc.2020.106250

    Article  Google Scholar 

  28. Rao, R.V.; Savsani, V.J.; Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43, 303–315 (2011). https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  29. Naik, B.; Nayak, J.; Behera, H.S.: A TLBO based gradient descent learning-functional link higher order ANN: An efficient model for learning from non-linear data. J. King Saud Univ. Comput. Inf. Sci. 30, 120–139 (2018). https://doi.org/10.1016/j.jksuci.2016.01.001

    Article  Google Scholar 

  30. Naumovi, M.B.; Veseli, B.R.: Magnetic levitation system in control engineering education. Autom. Control Robot. 7, 151–160 (2008)

    Google Scholar 

  31. Morales, R.; Feliu, V.; Member, S.; Sira-ramírez, H.; Member, S.: Nonlinear control for magnetic levitation systems based on fast online algebraic identification of the input gain. IEEE Trans. Control Syst. Technol. 19, 757–771 (2011)

    Article  Google Scholar 

  32. Balko, P.; Rosinova, D.: Modeling of magnetic levitation system. In: International Conference on Process Control. pp. 252–257 (2017)

  33. Liceaga-castro, J.; Hernandez-alcantara, D.; Amezquita-brooks, L.: Nonlinear control of a magnetic levitation system. In: Electronics, Robotics and Automotive Mechanics Conference, pp. 391–396 (2009). https://doi.org/10.1109/CERMA.2009.10

  34. Magnetic Levitation: Control Experiments Feedback Instruments Limited (2011)

  35. Ghosh, A.; Krishnan, T.R.; Tejaswy, P.; Mandal, A.; Pradhan, J.K.; Ranasingh, S.: Design and implementation of a 2-DOF PID compensation for magnetic levitation systems. ISA Trans. 53, 1216–1222 (2014)

    Article  Google Scholar 

  36. Swain, S.K.; Sain, D.; Kumar, S.; Ghosh, S.: Real time implementation of fractional order PID controllers for a magnetic levitation plant. Int. J. Electron. Commun. 78, 141–156 (2017)

    Article  Google Scholar 

  37. Yaghini, M.; Khoshraftar, M.M.; Fallahi, M.: A hybrid algorithm for artificial neural network training. Eng. Appl. Artif. Intell. 26(1), 293–301 (2013)

    Article  Google Scholar 

  38. Patra, J.C.; Kot, A.C.: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans. Syst. Man. Cybern. 32, 505–511 (2002). https://doi.org/10.1109/TSMCB.2002.1018769

    Article  Google Scholar 

  39. Subudhi, B.; Jena, D.: Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing. 74, 1696–1709 (2011). https://doi.org/10.1016/j.neucom.2011.02.006

    Article  Google Scholar 

  40. Katari, V.; Malireddi, S.; Satya, S.K.; Panda, G.: Adaptive nonlinear system identification using comprehensive learning PSO. In: International Symposium on Communications, Control and Signal Processing, pp. 434–439 (2008). https://doi.org/10.1109/ISCCSP.2008.4537265

  41. Juang, J.-G.; Lin, B.-S.: Nonlinear system identification by evolutionary computation and recursive estimation method. In: American Control Conference, pp. 5073–5078 (2005). https://doi.org/10.1109/CINE.2015.22

  42. Puchta, E.D.P.; Siqueira, H.V.; Kaster, M.D.S.: Optimization tools based on metaheuristics for performance enhancement in a gaussian adaptive PID controller. IEEE Trans. Cybern. 50, 1185–1194 (2020). https://doi.org/10.1109/TCYB.2019.2895319

    Article  Google Scholar 

  43. Rao, R.V.; Savsani, V.J.; Balic, J.: Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng. Optim. 44, 1447–1462 (2012). https://doi.org/10.1080/0305215X.2011.652103

    Article  Google Scholar 

  44. Kumar, M.; Mishra, S.K.: Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image. Bio Med. Mater. Eng. 28, 643–654 (2017)

    Article  Google Scholar 

  45. Singh, S.; Ashok, A.; Kumar, M.; Rawat, T.K.: Adaptive infinite impulse response system identification using teacher learner based optimization algorithm. Appl. Intell. 49, 1785–1802 (2018). https://doi.org/10.1007/s10489-018-1354-4

    Article  Google Scholar 

  46. Patra, J.C.; Kot, A.C.: Nonlinear dynamic system identification using chebyshev functional link artificial neural network. In: IEEE Transactions on Systems, Man, and Cybernetics, vol. 32, pp, 505–511 (2002)

  47. Li, M., He, Y.: Nonlinear system identification using adaptive Chebyshev neural networks. IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 243–247 (2010)

  48. Nanda, S.J., Panda, G., Majhi, B., Tah, P.: Improved Identification of Nonlinear MIMO Plants using New Hybrid FLANN-AIS Model. In: International Advanced Computing Conference, pp. 141–146 (2009). https://doi.org/10.1109/IADCC.2009.4808996

  49. Kumar, M.; Mishra, S.K.: Particle swarm optimization-based functional link artificial neural network for medical image denoising. In: Computational Vision and Robotics, vol. 105–111 (2015)

  50. Arora, A.; Hote, Y.V.; Rastogi, M.: Design of PID controller for unstable system. Commun. Comput. Inf. Sci. 140, 19–26 (2011). https://doi.org/10.1007/978-3-642-19263-0_3

    Article  Google Scholar 

  51. Rastogi, M.A.; Arora, Y.V.H.: Design of Fuzzy Logic Based PID Controller for an Unstable System, Vol. 157, p. 66–571. Springer, Berlin (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of EEE, Centurion University of Technology and Management, Bhubaneswar, Odisha, India

    Amit Kumar Sahoo

  2. Department of EEE, Birla Institute of Technology, Mesra, Ranchi, India

    Sudhansu Kumar Mishra

  3. G. G. Vishwavidyalaya (A Central University), Bilaspur, CG, 495009, India

    Babita Majhi

  4. C. V. Raman Global University, Bhubaneswar, India

    Ganapati Panda

  5. KIIT University, Bhubaneswar, India

    Suresh Chandra Satapathy

Authors
  1. Amit Kumar Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudhansu Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Babita Majhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ganapati Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Suresh Chandra Satapathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suresh Chandra Satapathy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahoo, A.K., Mishra, S.K., Majhi, B. et al. Real-Time Identification of Fuzzy PID-Controlled Maglev System using TLBO-Based Functional Link Artificial Neural Network. Arab J Sci Eng 46, 4103–4118 (2021). https://doi.org/10.1007/s13369-020-05292-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05292-x

Keywords

Search

Navigation