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Doğrusal olmayan manyetik levitasyon sisteminin kontrolünde PID ve kendini ayarlayan bulanık PID kontrol yöntemlerinin uygulaması

Year 2024, , 514 - 529, 31.07.2024
https://doi.org/10.61112/jiens.1420710

Abstract

Manyetik Levitasyon Sisteminin (MLS) düşük enerji tüketimi ve az sürtünmesi nedeniyle, kararsız ve doğrusal olmayan sistemler için hayati önem taşıyan iki faktörden dolayı MLS araştırmaları artık mühendislik alanında yürütülmektedir. Bu makale, MLS'nin yapısının karmaşıklığı ve kontrol edilebilirlik zorluklarıyla başa çıkmak için ileri kontrol teorileri uygulanarak kullanılan kontrol teorilerinin performansının karşılaştırılmasını tartışmaktadır. Karşılaştırılan kontrol yöntemleri Oransal-İntegral-Türev (PID) ve Kendini Ayarlayan Bulanık PID (STFPID) yöntemleridir. Bu yöntemler MATLAB ortamında geliştirilmiştir. MATLAB ortamında oluşturulan MLS modeli önerilen kontrol yöntemlerine tabi tutularak sonuçlar karşılaştırılmıştır. Sonuçlar, MLS konum kontrolünün PID ve STFPID tekniklerinden yararlanabileceğini açıkça göstermektedir. Geliştirilen kontrol yaklaşımlarının performanslarını karşılaştırmak için dört kriter kullanıldı. Kriterler bunlar; yükselme süresi, yerleşme süresi, maksimum aşma yüzdesi ve aşma değeri. STFPID kontrol yönteminin PID kontrol yöntemine göre daha kararlı bir şekilde MLS kontrolünü sağladığı sonuçlarda açıkça görülmektedir.

References

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  • Yang B, Liu Z, Liu H, Li Y, Lin S (2020) A GPC-based multi-variable PID control algorithm and its application in anti-swing control and accurate positioning control for bridge cranes. Int J Control Autom. Syst. https://doi.org/10.1007/s12555-019-0400-2
  • Zhang C, Wu X, Xu J (2021) Particle swarm sliding mode-fuzzy PID control based on maglev system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3095490
  • Sio KC, Lee CK (1998) Stability of fuzzy PID controllers. IEEE Trans Syst Man Cybern Part A Syst Humans. https://doi.org/10.1109/3468.686710
  • Dai A, Zhou X, Liu X (2017) Design and simulation of a genetically optimized fuzzy immune PID controller for a novel grain dryer. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2733760
  • Moura JP, Fonseca JV, Rego PHM (2019) A neuro-fuzzy model for online optimal tuning of PID controllers in industrial system applications to the mining sector. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2019.2923963
  • Osinski C, Leandro GV, Costa Oliveira GH (2019) Fuzzy PID controller design for LFC in electric power systems. IEEE Lat Am Trans. https://doi.org/10.1109/TLA.2019.8826706
  • Lin CM, Lin MH, Chen CW (2011) SoPC-based adaptive PID control system design for magnetic levitation system. IEEE Syst J. https://doi.org/10.1109/JSYST.2011.2134530
  • Ishaque K, Saleem Y, Abdullah SS, Amjad M, Rashid M, Kazi S (2011) Modeling and control of magnetic levitation system via fuzzy logic controller. Fourth International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia, Apr. 19-21.
  • Ahmad Z, Umar M, Shaukat S, Hassan S, Lupin S (2020) Design and performance enhancement of a single axis magnetic levitation system using fuzzy supervised PID. IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference, St. Petersburg and Moscow, Russia, Jan. 27-30.
  • Swain SK, Sain D, Mishra SK, Ghosh S (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. AEU Int J Electron Commun. https://doi.org/10.1016/j.aeue.2017.05.029
  • Wijesinghe S, Vithanawasam TMW, Priyankara H (2018) Fuzzy logic controller vs PID controller for real time magnetic levitation system. IEEE International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, Dec. 21-22.
  • Ahmad I, Shahzad M, Palensky P (2014) Optimal PID control of magnetic levitation system using genetic algorithm. IEEE International Energy Conference, Cavtat, Croatia, May. 13-16.
  • Çeven S, Albayrak A (2020) Çift ters sarkaç sisteminin kontrolü için PID ve LQR kontrolcü tasarımlarının modellenmesi. Eur J Sci Technol. https://doi.org/10.31590/ejosat.780070
  • Çeven S, Albayrak A, Bayır R (2020) Real-time range estimation in electric vehicles using fuzzy logic classifier. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2020.106577
  • Duran F, Ceven S, Bayir R (2018) Drive mode estimation for electric vehicles via fuzzy logic. 22nd International Conference Electronics, Palanga, Lithuania, Jun. 18-20.
  • Uysal A, Gokay S, Soylu E, Soylu T, Çaşka S (2019) Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication. Meas Control. https://doi.org/10.1177/0020294019858188
  • Han Y, Yao X, Yang Y (2024) Disturbance rejection tube model predictive levitation control of maglev trains. High-speed Railway. https://doi.org/10.1016/j.hspr.2024.01.001
  • Kumar B, Swain SK, Mishra SK, Singh YK, Ghosh S (2024) Radial Basis Function-based Adaptive Gain Super-Twisting Controller for Magnetic Levitation System with Time-Varying External Disturbance. IEEE Trans Transp Electrif. https://doi.org/10.1109/TTE.2024.3354795
  • Li W, Fan K, Wu Z (2024) Magnetic levitation system control research based on improved linear active disturbance rejection. Trans Inst Meas Control. https://doi.org/10.1177/01423312241229838
  • Pandey A, Adhyaru DM (2024) Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty. Int J Dyn Control. https://doi.org/10.1007/s40435-024-01412-9
  • Xu Z, Trakarnchaiyo C, Stewart C, Khamesee MB (2024) Modular Maglev: Design and implementation of a modular magnetic levitation system to levitate a 2D Halbach array. Mechatron. https://doi.org/10.1016/j.mechatronics.2024.103148
  • Zhu Q, Wang SM, Ni YQ (2024) A Review of Levitation Control Methods for Low-and Medium-Speed Maglev Systems. Build. https://doi.org/10.3390/buildings14030837
  • Hernández-Alvarado R, García-Valdovinos LG, Salgado-Jiménez T, Gómez-Espinosa A, Fonseca-Navarro F (2016) Neural network-based self-tuning PID control for underwater vehicles. Sens. https://doi.org/10.3390/s16091429
  • Meza JL, Santibáñez V, Soto R, Llama MA (2011) Fuzzy self-tuning PID semiglobal regulator for robot manipulators. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2011.2168789
  • Refaat A, Elbaz A, Khalifa AE, Elsakka MM, Kalas A, Elfar MH (2024) Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions. Energy Convers Manage. https://doi.org/10.1016/j.enconman.2023.118014
  • Cedro L, Wieczorkowski K, Szcześniak A (2024) An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter. Acta Mech Auto. https://doi.org/10.2478/ama-2024-0004
  • Blanck-Kahan D, Ortiz-Cervantes G, Martínez-Gama V, Cervantes-Culebro H, Chong-Quero JE, Cruz-Villar CA (2024) Neural-optimal tuning of a controller for a parallel robot. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2023.121184
  • Dhundhara S, Arya Y, Bansal RC (2024) In Advanced Frequency Regulation Strategies in Renewable Dominated Power Systems. In: Priyadarshani S (ed) Design of an I+ Fuzzy based PD control strategy for damping power system oscillations in a networked environment integrated with renewable energy sources, Elsevier, United Kingdom, ss 93-121
  • 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. https://doi.org/10.1016/j.engappai.2023.107645
  • Dey S, Banerjee S, Dey J (2024) Optimum Tuning of 1&2-dof TID-F Controllers for a MAGLEV System with Experimental Validation. Third International Conference on Power, Control and Computing Technologies, Raipur, India, Jan. 18-20.
  • Liu L, Yau JD, Qin J, Urushadze S (2021) Optimal dynamic control for a maglev vehicle moving on multi-span guideway girders. J Mech. https://doi.org/10.1093/jom/ufab006
  • Kuo B. C (1987) Automatic control systems. Prentice Hall PTR, New Jersey
  • Isidori A (1985) Nonlinear control systems: an introduction. Springer, Berlin
  • Ahmad I, Javaid MA (2010) Nonlinear model & controller design for magnetic levitation system. Recent advances in signal processing, robotics and automation, Cambridge, United Kingdom, Feb. 20-22.
  • Ziegler JG, Nichols NB (1942) Optimum settings for automatic controllers. ASME J Fluids Eng. https://doi.org/10.1115/1.4019264

Application of PID and self-tuning fuzzy PID control methods in the control of non-linear magnetic levitation system

Year 2024, , 514 - 529, 31.07.2024
https://doi.org/10.61112/jiens.1420710

Abstract

Because of the Magnetic Levitation System's (MLS) low energy consumption and little friction two factors that are deemed crucial for unstable and nonlinear systems MLS research is now being conducted in the engineering area. This article discusses the comparison of the performance of control theories used by applying advanced control theories to cope with the complexity of the structure and controllability difficulties of MLS. The control methods compared are Proportional–Integral–Derivative (PID) and Self-Tuning Fuzzy PID (STFPID) methods. These methods were developed in the MATLAB environment. The MLS model created in the MATLAB environment was subjected to the suggested control methods, and the outcomes were compared. The outcomes unequivocally demonstrate that MLS location control may make use of PID and STFPID techniques. Four criteria were used to compare the developed control approaches performances. These are the criteria; rise time, settling time, percent maximum overshoot and overshoot value. It is clearly seen in the results that the STFPID control method provides control of the MLS with greater stability than the PID control method.

References

  • Alkurawy L, Mohammed K (2020) Model predictive control of magnetic levitation system. Int J Electr Comput Eng. http://doi.org/10.11591/ijece.v10i6.pp5802-5812
  • Gutierrez H, Luijten H (2018) 5-DOF real-time control of active electrodynamic MAGLEV. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2018.2795520
  • Yang B, Liu Z, Liu H, Li Y, Lin S (2020) A GPC-based multi-variable PID control algorithm and its application in anti-swing control and accurate positioning control for bridge cranes. Int J Control Autom. Syst. https://doi.org/10.1007/s12555-019-0400-2
  • Zhang C, Wu X, Xu J (2021) Particle swarm sliding mode-fuzzy PID control based on maglev system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3095490
  • Sio KC, Lee CK (1998) Stability of fuzzy PID controllers. IEEE Trans Syst Man Cybern Part A Syst Humans. https://doi.org/10.1109/3468.686710
  • Dai A, Zhou X, Liu X (2017) Design and simulation of a genetically optimized fuzzy immune PID controller for a novel grain dryer. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2733760
  • Moura JP, Fonseca JV, Rego PHM (2019) A neuro-fuzzy model for online optimal tuning of PID controllers in industrial system applications to the mining sector. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2019.2923963
  • Osinski C, Leandro GV, Costa Oliveira GH (2019) Fuzzy PID controller design for LFC in electric power systems. IEEE Lat Am Trans. https://doi.org/10.1109/TLA.2019.8826706
  • Lin CM, Lin MH, Chen CW (2011) SoPC-based adaptive PID control system design for magnetic levitation system. IEEE Syst J. https://doi.org/10.1109/JSYST.2011.2134530
  • Ishaque K, Saleem Y, Abdullah SS, Amjad M, Rashid M, Kazi S (2011) Modeling and control of magnetic levitation system via fuzzy logic controller. Fourth International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia, Apr. 19-21.
  • Ahmad Z, Umar M, Shaukat S, Hassan S, Lupin S (2020) Design and performance enhancement of a single axis magnetic levitation system using fuzzy supervised PID. IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference, St. Petersburg and Moscow, Russia, Jan. 27-30.
  • Swain SK, Sain D, Mishra SK, Ghosh S (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. AEU Int J Electron Commun. https://doi.org/10.1016/j.aeue.2017.05.029
  • Wijesinghe S, Vithanawasam TMW, Priyankara H (2018) Fuzzy logic controller vs PID controller for real time magnetic levitation system. IEEE International Conference on Information and Automation for Sustainability, Colombo, Sri Lanka, Dec. 21-22.
  • Ahmad I, Shahzad M, Palensky P (2014) Optimal PID control of magnetic levitation system using genetic algorithm. IEEE International Energy Conference, Cavtat, Croatia, May. 13-16.
  • Çeven S, Albayrak A (2020) Çift ters sarkaç sisteminin kontrolü için PID ve LQR kontrolcü tasarımlarının modellenmesi. Eur J Sci Technol. https://doi.org/10.31590/ejosat.780070
  • Çeven S, Albayrak A, Bayır R (2020) Real-time range estimation in electric vehicles using fuzzy logic classifier. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2020.106577
  • Duran F, Ceven S, Bayir R (2018) Drive mode estimation for electric vehicles via fuzzy logic. 22nd International Conference Electronics, Palanga, Lithuania, Jun. 18-20.
  • Uysal A, Gokay S, Soylu E, Soylu T, Çaşka S (2019) Fuzzy proportional-integral speed control of switched reluctance motor with MATLAB/Simulink and programmable logic controller communication. Meas Control. https://doi.org/10.1177/0020294019858188
  • Han Y, Yao X, Yang Y (2024) Disturbance rejection tube model predictive levitation control of maglev trains. High-speed Railway. https://doi.org/10.1016/j.hspr.2024.01.001
  • Kumar B, Swain SK, Mishra SK, Singh YK, Ghosh S (2024) Radial Basis Function-based Adaptive Gain Super-Twisting Controller for Magnetic Levitation System with Time-Varying External Disturbance. IEEE Trans Transp Electrif. https://doi.org/10.1109/TTE.2024.3354795
  • Li W, Fan K, Wu Z (2024) Magnetic levitation system control research based on improved linear active disturbance rejection. Trans Inst Meas Control. https://doi.org/10.1177/01423312241229838
  • Pandey A, Adhyaru DM (2024) Robust-optimal control design for current-controlled electromagnetic levitation system with unmatched input uncertainty. Int J Dyn Control. https://doi.org/10.1007/s40435-024-01412-9
  • Xu Z, Trakarnchaiyo C, Stewart C, Khamesee MB (2024) Modular Maglev: Design and implementation of a modular magnetic levitation system to levitate a 2D Halbach array. Mechatron. https://doi.org/10.1016/j.mechatronics.2024.103148
  • Zhu Q, Wang SM, Ni YQ (2024) A Review of Levitation Control Methods for Low-and Medium-Speed Maglev Systems. Build. https://doi.org/10.3390/buildings14030837
  • Hernández-Alvarado R, García-Valdovinos LG, Salgado-Jiménez T, Gómez-Espinosa A, Fonseca-Navarro F (2016) Neural network-based self-tuning PID control for underwater vehicles. Sens. https://doi.org/10.3390/s16091429
  • Meza JL, Santibáñez V, Soto R, Llama MA (2011) Fuzzy self-tuning PID semiglobal regulator for robot manipulators. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2011.2168789
  • Refaat A, Elbaz A, Khalifa AE, Elsakka MM, Kalas A, Elfar MH (2024) Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions. Energy Convers Manage. https://doi.org/10.1016/j.enconman.2023.118014
  • Cedro L, Wieczorkowski K, Szcześniak A (2024) An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter. Acta Mech Auto. https://doi.org/10.2478/ama-2024-0004
  • Blanck-Kahan D, Ortiz-Cervantes G, Martínez-Gama V, Cervantes-Culebro H, Chong-Quero JE, Cruz-Villar CA (2024) Neural-optimal tuning of a controller for a parallel robot. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2023.121184
  • Dhundhara S, Arya Y, Bansal RC (2024) In Advanced Frequency Regulation Strategies in Renewable Dominated Power Systems. In: Priyadarshani S (ed) Design of an I+ Fuzzy based PD control strategy for damping power system oscillations in a networked environment integrated with renewable energy sources, Elsevier, United Kingdom, ss 93-121
  • 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. https://doi.org/10.1016/j.engappai.2023.107645
  • Dey S, Banerjee S, Dey J (2024) Optimum Tuning of 1&2-dof TID-F Controllers for a MAGLEV System with Experimental Validation. Third International Conference on Power, Control and Computing Technologies, Raipur, India, Jan. 18-20.
  • Liu L, Yau JD, Qin J, Urushadze S (2021) Optimal dynamic control for a maglev vehicle moving on multi-span guideway girders. J Mech. https://doi.org/10.1093/jom/ufab006
  • Kuo B. C (1987) Automatic control systems. Prentice Hall PTR, New Jersey
  • Isidori A (1985) Nonlinear control systems: an introduction. Springer, Berlin
  • Ahmad I, Javaid MA (2010) Nonlinear model & controller design for magnetic levitation system. Recent advances in signal processing, robotics and automation, Cambridge, United Kingdom, Feb. 20-22.
  • Ziegler JG, Nichols NB (1942) Optimum settings for automatic controllers. ASME J Fluids Eng. https://doi.org/10.1115/1.4019264
There are 37 citations in total.

Details

Primary Language English
Subjects Control Engineering
Journal Section Research Articles
Authors

Yusuf Karabacak 0000-0001-9864-7512

Publication Date July 31, 2024
Submission Date January 16, 2024
Acceptance Date May 31, 2024
Published in Issue Year 2024

Cite

APA Karabacak, Y. (2024). Application of PID and self-tuning fuzzy PID control methods in the control of non-linear magnetic levitation system. Journal of Innovative Engineering and Natural Science, 4(2), 514-529. https://doi.org/10.61112/jiens.1420710


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