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Constrained control for electromagnetic levitation system with matched and mismatched uncertainties via robust optimal design

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Abstract

In the magnetic levitation (Maglev) train and in many levitation applications, the electromagnet levitation system (EMLS) is the main control element. Its highly nonlinear dynamics and innate instability define its nature. Furthermore, the controller design for this system is further complicated by many external perturbations and modelling uncertainties. To fade away from this drawback, a constrained control for an electromagnetic levitation system with matched and mismatched uncertainties via robust optimal design is proposed in this article. The optimal control approach based on the Hamilton–Jacobi–Bellman (HJB) equation is designed for the bounded robust control problem. A non-quadratic term is added in the performance function to address the range of the constraint control input incorporated by the (HJB) equation. The parametric uncertainties and external disturbances are directly addressed in the cost function. The direct Lyapunov stability theorem is utilised to prove the optimality of the designed controller concerning the performance function that incorporates the additional control effort and highest bound on the matched and mismatched uncertainties. The upper bound of the system uncertainties should be known to the constrained control effort. The stability is proved using the actual dynamics of the EMS. The results obtained from the simulation demonstrate the performance of the proposed controller design. The integral performance indices are compared for matched and mismatched uncertainties with and without constrained control to highlight the resilience of the proposed control technique.

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References

  1. Adil HMM, Ahmed S, Ahmad I (2020) Control of MagLev system using supertwisting and integral backstepping sliding mode algorithm. IEEE Access 8:51352–51362

    Article  Google Scholar 

  2. Pandey A, Adhyaru DM (2023) Control techniques for electromagnetic levitation system: a literature review. Int J Dyn Control 11(1):441–451

    Article  Google Scholar 

  3. Sun X, Dou R, Sun F, Wang K, Jin J. (2016) Analysis of magnetic driven non bearing oil free scroll compressor. In 2016 IEEE advanced information management, communicates, electronic and automation control conference (IMCEC) (pp. 2032-2036). IEEE

  4. Eroğlu Y, Ablay G (2016) Cascade sliding mode-based robust tracking control of a magnetic levitation system. Proc Inst Mechan Eng Part I J Syst Control Eng 230(8):851–860

    Google Scholar 

  5. Bidikli B, Bayrak A (2018) A self-tuning robust full-state feedback control design for the magnetic levitation system. Control Eng Pract 78:175–185

    Article  Google Scholar 

  6. Afshar KK, Javadi A (2021) Mass estimation and adaptive output feedback control of nonlinear electromagnetic levitation system. J Sound Vib 495:115923

    Article  Google Scholar 

  7. Abdollahzadeh M, Pourgholi M (2024) Adaptive fuzzy sliding mode control of magnetic levitation system based on Interval Type-2 Fuzzy Neural Network Identification with an Extended Kalman-Bucy filter. Eng Appl Artif Intell 130:107645

    Article  Google Scholar 

  8. Sagar A, Radhakrishnan R, Raja GL (2023) Experimentally validated frequency shifted internal model cascade control strategy for magnetic levitation system. IFAC J Syst Control 26:100234

    Article  Google Scholar 

  9. Wiboonjaroen W, Sujitjorn S (2013) State-PID feedback for magnetic levitation system. Adv Mater Res 622:1467–1473

    Google Scholar 

  10. Abdulwahhab OW (2020) Design of an adaptive state feedback controller for a magnetic levitation system. Int J Electr Comput Eng 10(5):4782

    Google Scholar 

  11. Yaseen HMS, Siffat SA, Ahmad I, Malik AS (2022) Nonlinear adaptive control of magnetic levitation system using terminal sliding mode and integral backstepping sliding mode controllers. ISA Trans 126:121–133

    Article  Google Scholar 

  12. Iswanto I, Maarif A (2020) Robust integral state feedback using coefficient diagram in magnetic levitation system. IEEE Access 8:57003–57011

    Article  Google Scholar 

  13. Morales R, Sira-Ramírez H (2010) Trajectory tracking for the magnetic ball levitation system via exact feedforward linearisation and GPI control. Int J Control 83(6):1155–1166

    Article  MathSciNet  Google Scholar 

  14. Hypiusová M, Rosinová D (2018) Discrete-time robust LMI pole placement for magnetic levitation. In 2018 Cybernetics & Informatics (K &I) (pp. 1-6). IEEE

  15. Winursito A, Pratama GNP (2021) LQR state feedback controller with precompensator for magnetic levitation system. J Phys Conf Ser 2111:012004

    Article  Google Scholar 

  16. Malik AS, Ahmad I, Rahman AU, Islam Y (2019) Integral backstepping and synergetic control of magnetic levitation system. IEEE Access 7:173230–173239

    Article  Google Scholar 

  17. Sun W, Wang X, Zhang C (2019) A model-free control strategy for vehicle lateral stability with adaptive dynamic programming. IEEE Trans Ind Electron 67(12):10693–10701

    Article  Google Scholar 

  18. Gandhi RV, Adhyaru DM (2019) Takagi-Sugeno fuzzy regulator design for nonlinear and unstable systems using negative absolute eigenvalue approach. IEEE/CAA J Autom Sin 7(2):482–493

    Article  Google Scholar 

  19. Abu Arqub O, Singh J, Maayah B, Alhodaly M (2023) Reproducing kernel approach for numerical solutions of fuzzy fractional initial value problems under the Mittag-Leffler kernel differential operator. Math Methods Appl Sci 46(7):7965–7986

    Article  MathSciNet  Google Scholar 

  20. Abu Arqub O, Singh J, Alhodaly M (2023) Adaptation of kernel functions-based approach with Atangana-Baleanu-Caputo distributed order derivative for solutions of fuzzy fractional Volterra and Fredholm integrodifferential equations. Math Methods Appl Sci 46(7):7807–7834

    Article  MathSciNet  Google Scholar 

  21. Sathiyavathi S (2019) Design of sliding mode controller for magnetic levitation system. Comput Electr Eng 78:184–203

    Article  Google Scholar 

  22. Wu Y, Yu X, Man Z (1998) Terminal sliding mode control design for uncertain dynamic systems. Syst Control Lett 34(5):281–287

    Article  MathSciNet  Google Scholar 

  23. Teklu EA, Abdissa CM (2023) Genetic algorithm tuned super twisting sliding mode controller for suspension of maglev train with flexible track. IEEE Access 11:30955–30969

    Article  Google Scholar 

  24. Alkurawy LEJ, Mohammed KG (2020) Model predictive control of magnetic levitation system. Int J Electr Comput Eng 10(6):5802–5812

    Google Scholar 

  25. Hu W, Zhou Y, Zhang Z, Fujita H (2021) Model predictive control for hybrid levitation systems of maglev trains with state constraints. IEEE Trans Vehic Technol 70(10):9972–9985

    Article  Google Scholar 

  26. Abdissa CM (2024) Improved model predictive speed control of a PMSM via Laguerre functions. Mathematical Problems in Engineering, 2024

  27. de Jesús Rubio J, Zhang L, Lughofer E, Cruz P, Alsaedi A, Hayat T (2017) Modeling and control with neural networks for a magnetic levitation system. Neurocomputing 227:113–121

    Article  Google Scholar 

  28. Madebo MM, Abdissa CM, Lemma LN, Negash DS (2024) Robust Tracking Control for Quadrotor UAV with External Disturbances and Uncertainties Using Neural Network Based MRAC. IEEE Access

  29. Maayah B, Arqub OA (2024) Uncertain M-fractional differential problems: existence, uniqueness, and approximations using Hilbert reproducing technique provisioner with the case application: series resistor-inductor circuit. Phys Scr 99(2):025220

    Article  Google Scholar 

  30. Abdollahzadeh M (2023) Adaptive dynamic programming discrete-time LQR control on electromagnetic levitation system. IET Control Theory & Applications

  31. Wang J, Zhao L, Yu L (2020) Adaptive terminal sliding mode control for magnetic levitation systems with enhanced disturbance compensation. IEEE Trans Ind Electron 68(1):756–766

    Article  MathSciNet  Google Scholar 

  32. Bosera AS, Olana FD, Merga C, Gutole ST (2022, November) Adaptive PSO based gain optimization of sliding mode control for position tracking control of magnetic levitation systems. In 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) (pp. 157-162). IEEE

  33. García-Gutiérrez G, Arcos-Aviles D, Carrera EV, Guinjoan F, Motoasca E, Ayala P, Ibarra A (2019) Fuzzy logic controller parameter optimization using metaheuristic cuckoo search algorithm for a magnetic levitation system. Appl Sci 9(12):2458

    Article  Google Scholar 

  34. Sun Y, Xu J, Qiang H, Chen C, Lin G (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141:217–226

    Article  Google Scholar 

  35. Abu Arqub O, Mezghiche R, Maayah B (2023) Fuzzy M-fractional integrodifferential models: theoretical existence and uniqueness results, and approximate solutions utilizing the Hilbert reproducing kernel algorithm. Front Phys 11:1252919

    Article  Google Scholar 

  36. Cheng T, Lewis FL, Abu-Khalaf M (2007) Fixed-final-time-constrained optimal control of nonlinear systems using neural network HJB approach. IEEE Trans Neural Netw 18(6):1725–1737

  37. Adhyaru DM, Kar IN, Gopal M (2009) Fixed final time optimal control approach for bounded robust controller design using Hamilton-Jacobi-Bellman solution. IET Control Theory Appl 3(9):1183–1195

    Article  MathSciNet  Google Scholar 

  38. Liao F, Ji H, Xie Y (2016) A nearly optimal control for spacecraft rendezvous with constrained controls. Trans Inst Measurem Contr 38(7):832–845

    Article  Google Scholar 

  39. Gopal M (1993) Modern control system theory. New Age International

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Acknowledgements

The manuscript discussed is a part of the full-time Ph.D. programme offered by the Institute of Technology, Nirma University, Ahmedabad, Gujarat, India.

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Correspondence to Dipak M. Adhyaru.

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Pandey, A., Adhyaru, D.M. Constrained control for electromagnetic levitation system with matched and mismatched uncertainties via robust optimal design. Int. J. Dynam. Control (2024). https://doi.org/10.1007/s40435-024-01435-2

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  • DOI: https://doi.org/10.1007/s40435-024-01435-2

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