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Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm

Yıl 2024, Cilt: 26 Sayı: 77, 291 - 298, 27.05.2024

İbrahim Şenaslan Boğaç Bilgiç

https://doi.org/10.21205/deufmd.2024267713

Öz

In this study, a filter has been incorporated to enhance the performance of the PID controller, which is commonly used for controlling suspension systems. While designing this filter, the insipration has been the low-pass filter used in sliding mode controllers to prevent chattering and uncertainties in system parameters, unlike conventional PID controller filters. Additionally, the filtered force value was combined with the force value obtained from the PID controller using an equation based on a coefficient, and filter coefficients were optimized through genetic algorithms. As a result of the optimization, the designed controller was simulated for various road inputs that could be encountered, and results were obtained. By comparing the results obtained with a PID controller without a filter and without a controller, the performance of the designed controller is clearly shown according to IAE and ISE criteria. Robustness of the controller was evaluated under varying mass conditions and its performance was given as a table.

Anahtar Kelimeler

Filter PID Genetic Algorithm Active Suspension Systems Robust Control

Kaynakça

  • Yu, B., Wang, Z., Wang, Z., et al. 2019. Investigation of the Suspension Design and Ride Comfort of an Electric Mini Off-road Vehicle, Advances in Mechanical Engineering, 11(1):1-10. DOI: 10.1177/1687814018823351.
  • Silva, R.R.M.R., Reinaldo, I.L., Montenegro, D.P., et al. 2021. Optimization of Vehicle Suspension Parameters based on Ride Comfort and Stability Requirements, Journal of Automobile Engineering, 235(7):1920-1929. DOI: 10.1177/0954407020983057.
  • Sert, E. 2017. Improvement of the Vehicle Stability Using Suspension Optimization Methods, International Journal of Automotive Engineering Technologies, 6(2):70-84.
  • Jazar, R.N. Vehicle Dynamics: Theory and Applications. Springer. 455.
  • Guglielmino, E., Sireteanu, T., Stammers C.W., et al. 2008. Semi-active Suspension Control, Improved Vehicle Ride and Road Friendliness. Springer-Verlag London Ltd. 302s. DOI: 10.1007/978-1-84800-231-9
  • Nagarkar, M., Bhalerao, Y., Bhaskar, D., et al. 2022. Design of Passive Suspension System to Mimic Fuzzy Logic Control Active Suspension System, Springer, 11:109. DOI: 10.1186/s43088-022-00291-3.
  • Yerrawar, R.N., Arakerimath, R.R. 2017. Development of Methodology for Semi Active Suspension System Using MR Damper, Elsevier, 4(8):9294-9303. DOI: 10.1016/j.matpr.2017.07.289.
  • Hyniova, K. 2022. Disturbance Rejection in a One-half Vehicle Suspension Using a Fuzzy Controller, International Scientific Journal, 7(3):98-102.
  • Soliman, AMA., Kaldas, MMS. 2019. Semi-active suspension systems from research to mass-market –A review, Journal of Low Frequency Noise, Vibration and Active Control, 40(2):1005-1023. DOI: 10.1177/1461348419876392.
  • Jayachandran, R., Krishnapillai, S. 2013. Modeling and optimization of passive and semi-active suspension systems for passenger cars to improve ride comfort and isolate engine vibration, Journal of Vibration and Control, 19(10):1471-1479. DOI: 10.1177/1077546312445199.
  • Ang, K.H., Chong, G. 2005. PID Control System Analysis, Design and Technology, IEEE Transactions on Control System Technology, 13(4):559-576. DOI: 10.1109/TCST.2005.847331.
  • Ergin, A.,Sandal, B., 2023. Mobbing among seafarers: Scale development and application of an interval type-2 fuzzy logic system, Ocean Engineering, 286:115595. DOI: 10.1016/j.oceaneng.2023.115595.
  • Gad, G.A. 2022. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review, Archives of Computational Methods in Engineering, 29:2531-2561. DOI: 10.1007/s11831-021-09694-4.
  • Huba, M., Chamraz, S., Bistak, P., et al. 2021. Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable, Sensors, 21(18): 6157. DOI: 10.3390/s21186157.
  • Hemati, A., Shooshtari, A. 2019. Suspension damping optimization using genetic algorithms, International Journal of Automotive Engineering and Technologies, 8(4): 178-185.
  • Çakır, M.F., Bayraktar, M. 2020. Modelling of Main Battle Tank and Designing LQR Controller to Decrease Weapon Oscillations, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(4):1861-1876. DOI: 10.17341/gazimmfd.660584.
  • Ümütlü, R.C., Öztürk, H., Bıdıklı, B. 2022. An Adaptive Controller Design for ATMD System Used in Structures Under the Effect of Unknown Nonlinear Effects, DEUFMD, 24(71): 571-579.
  • Jiregna, I., Sirata, G. 2020. A review of the vehicle suspension system, Journal of Mechanical and Energy Engineering, 4(2): 109-114. DOI: 10.30464/jmee.2020.4.2.109.
  • Theunissen, J., Tota, A., Gruber, P., et al. 2021. Preview-based techniques for vehicle suspension control: a state-of-the-art review, Annual Reviews in Control, 51: 1367-5788. DOI: 10.1016/j.arcontrol.2021.03.010.
  • Palanisamy, S., Karuppan, S. 2016. Fuzzy Control of Active Suspensiyon System, Journal of Vibroengineering, 18(5): 3197-3204. DOI: 10.21595/jve.2016.16699.
  • International Standard. 2016. Mechanical vibration — Road surface profiles — Reporting of measured data. (ISO standard no. 8608:2016.)
  • Loprencipe, G., Zoccali, P. 2017. Use of Generated Artificial Road Profiles in Road Roughness Evaluation, J. Mod. Transport, 25(1): 24-33. DOI: 10.1007/s40534-017-0122-1.
  • Utkin, V., Guldner, J., Shi, J., 2009. Slding Mode Control in Electro-Mechanical Systems. 2nd. Boca Raton, London: CRC Press, Taylor & Francis, 503s. DOI: 10.1201/9781420065619.
  • Zorlu, H., Sunca, Ş. 2017. Genetik Algoritma Kullanılarak Ağırlıklandırılmış Myriad Filtrelerin Optimizasyonu, International Journal of Multidisciplinary Studies and Innovative Technologies, 1(1): 9-14.
  • Taşpınar, T., Orman, Z. 2023. Genetik Algoritmalar ile Deniz Taşımacılığında Hız Optimizasyonu, İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi, 3(2): 82-97.

Genetik Algoritma ile Optimize Edilmiş Alçak Geçiren Filtre içeren PID Denetleyici Kullanılarak Süspansiyon Sistemi Kontrol Performansının Artırılması

Yıl 2024, Cilt: 26 Sayı: 77, 291 - 298, 27.05.2024

İbrahim Şenaslan Boğaç Bilgiç

https://doi.org/10.21205/deufmd.2024267713

Öz

Bu çalışmada, süspansiyon sistemlerinin kontrolü için yaygın olarak kullanılan PID kontrolcünün performansını arttırmak için bir filtre eklenmiştir. Bu filtre tasarlanırken, klasik PID kontrolcü filtrelerinden farklı olarak, kayan kipli kontrolcülerde çatırtıyı ve sistem parametrelerindeki belirsizlikleri önlemek için kullanılan düşük geçirgen filtreden esinlenilmiştir. Ayrıca filtrelenen kuvvet değeri, katsayıya dayalı bir denklem kullanılarak PID kontrolcüden elde edilen kuvvet değeri ile birleştirilmiş ve filtre katsayıları genetik algoritma aracılığıyla optimize edilmiştir. Yapılan optimizasyon sonucunda tasarlanan kontrolcü, karşılaşılabilecek çeşitli yol girişleri için simule edilerek sonuçlar elde edilmiştir. Kontrolcüsüz ve filtresiz PID kontrolcü ile elde edilen sonuçlar kıyaslanarak, tasarlanan kontrolcünün başarımı IAE ve ISE kriterlerine göre açıkça gösterilmiştir. Kontrolcünün gürbüzlüğü değişen kütle koşulları altında değerlendirilmiş ve performansı tablo halinde verilmiştir.

Anahtar Kelimeler

Filtreli PID Genetik Algoritma Aktif Süspansiyon Sistemleri Gürbüz Kontrol

Kaynakça

  • Yu, B., Wang, Z., Wang, Z., et al. 2019. Investigation of the Suspension Design and Ride Comfort of an Electric Mini Off-road Vehicle, Advances in Mechanical Engineering, 11(1):1-10. DOI: 10.1177/1687814018823351.
  • Silva, R.R.M.R., Reinaldo, I.L., Montenegro, D.P., et al. 2021. Optimization of Vehicle Suspension Parameters based on Ride Comfort and Stability Requirements, Journal of Automobile Engineering, 235(7):1920-1929. DOI: 10.1177/0954407020983057.
  • Sert, E. 2017. Improvement of the Vehicle Stability Using Suspension Optimization Methods, International Journal of Automotive Engineering Technologies, 6(2):70-84.
  • Jazar, R.N. Vehicle Dynamics: Theory and Applications. Springer. 455.
  • Guglielmino, E., Sireteanu, T., Stammers C.W., et al. 2008. Semi-active Suspension Control, Improved Vehicle Ride and Road Friendliness. Springer-Verlag London Ltd. 302s. DOI: 10.1007/978-1-84800-231-9
  • Nagarkar, M., Bhalerao, Y., Bhaskar, D., et al. 2022. Design of Passive Suspension System to Mimic Fuzzy Logic Control Active Suspension System, Springer, 11:109. DOI: 10.1186/s43088-022-00291-3.
  • Yerrawar, R.N., Arakerimath, R.R. 2017. Development of Methodology for Semi Active Suspension System Using MR Damper, Elsevier, 4(8):9294-9303. DOI: 10.1016/j.matpr.2017.07.289.
  • Hyniova, K. 2022. Disturbance Rejection in a One-half Vehicle Suspension Using a Fuzzy Controller, International Scientific Journal, 7(3):98-102.
  • Soliman, AMA., Kaldas, MMS. 2019. Semi-active suspension systems from research to mass-market –A review, Journal of Low Frequency Noise, Vibration and Active Control, 40(2):1005-1023. DOI: 10.1177/1461348419876392.
  • Jayachandran, R., Krishnapillai, S. 2013. Modeling and optimization of passive and semi-active suspension systems for passenger cars to improve ride comfort and isolate engine vibration, Journal of Vibration and Control, 19(10):1471-1479. DOI: 10.1177/1077546312445199.
  • Ang, K.H., Chong, G. 2005. PID Control System Analysis, Design and Technology, IEEE Transactions on Control System Technology, 13(4):559-576. DOI: 10.1109/TCST.2005.847331.
  • Ergin, A.,Sandal, B., 2023. Mobbing among seafarers: Scale development and application of an interval type-2 fuzzy logic system, Ocean Engineering, 286:115595. DOI: 10.1016/j.oceaneng.2023.115595.
  • Gad, G.A. 2022. Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review, Archives of Computational Methods in Engineering, 29:2531-2561. DOI: 10.1007/s11831-021-09694-4.
  • Huba, M., Chamraz, S., Bistak, P., et al. 2021. Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable, Sensors, 21(18): 6157. DOI: 10.3390/s21186157.
  • Hemati, A., Shooshtari, A. 2019. Suspension damping optimization using genetic algorithms, International Journal of Automotive Engineering and Technologies, 8(4): 178-185.
  • Çakır, M.F., Bayraktar, M. 2020. Modelling of Main Battle Tank and Designing LQR Controller to Decrease Weapon Oscillations, Journal of the Faculty of Engineering and Architecture of Gazi University, 35(4):1861-1876. DOI: 10.17341/gazimmfd.660584.
  • Ümütlü, R.C., Öztürk, H., Bıdıklı, B. 2022. An Adaptive Controller Design for ATMD System Used in Structures Under the Effect of Unknown Nonlinear Effects, DEUFMD, 24(71): 571-579.
  • Jiregna, I., Sirata, G. 2020. A review of the vehicle suspension system, Journal of Mechanical and Energy Engineering, 4(2): 109-114. DOI: 10.30464/jmee.2020.4.2.109.
  • Theunissen, J., Tota, A., Gruber, P., et al. 2021. Preview-based techniques for vehicle suspension control: a state-of-the-art review, Annual Reviews in Control, 51: 1367-5788. DOI: 10.1016/j.arcontrol.2021.03.010.
  • Palanisamy, S., Karuppan, S. 2016. Fuzzy Control of Active Suspensiyon System, Journal of Vibroengineering, 18(5): 3197-3204. DOI: 10.21595/jve.2016.16699.
  • International Standard. 2016. Mechanical vibration — Road surface profiles — Reporting of measured data. (ISO standard no. 8608:2016.)
  • Loprencipe, G., Zoccali, P. 2017. Use of Generated Artificial Road Profiles in Road Roughness Evaluation, J. Mod. Transport, 25(1): 24-33. DOI: 10.1007/s40534-017-0122-1.
  • Utkin, V., Guldner, J., Shi, J., 2009. Slding Mode Control in Electro-Mechanical Systems. 2nd. Boca Raton, London: CRC Press, Taylor & Francis, 503s. DOI: 10.1201/9781420065619.
  • Zorlu, H., Sunca, Ş. 2017. Genetik Algoritma Kullanılarak Ağırlıklandırılmış Myriad Filtrelerin Optimizasyonu, International Journal of Multidisciplinary Studies and Innovative Technologies, 1(1): 9-14.
  • Taşpınar, T., Orman, Z. 2023. Genetik Algoritmalar ile Deniz Taşımacılığında Hız Optimizasyonu, İleri Mühendislik Çalışmaları ve Teknolojileri Dergisi, 3(2): 82-97.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

İbrahim Şenaslan 0000-0002-8789-489X

Boğaç Bilgiç 0000-0003-1156-8841

Erken Görünüm Tarihi 14 Mayıs 2024
Yayımlanma Tarihi 27 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 26 Sayı: 77

Kaynak Göster

APA Şenaslan, İ., & Bilgiç, B. (2024). Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 26(77), 291-298. https://doi.org/10.21205/deufmd.2024267713
AMA Şenaslan İ, Bilgiç B. Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm. DEUFMD. Mayıs 2024;26(77):291-298. doi:10.21205/deufmd.2024267713
Chicago Şenaslan, İbrahim, ve Boğaç Bilgiç. “Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated With Genetic Algorithm”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 26, sy. 77 (Mayıs 2024): 291-98. https://doi.org/10.21205/deufmd.2024267713.
EndNote Şenaslan İ, Bilgiç B (01 Mayıs 2024) Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 26 77 291–298.
IEEE İ. Şenaslan ve B. Bilgiç, “Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm”, DEUFMD, c. 26, sy. 77, ss. 291–298, 2024, doi: 10.21205/deufmd.2024267713.
ISNAD Şenaslan, İbrahim - Bilgiç, Boğaç. “Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated With Genetic Algorithm”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 26/77 (Mayıs 2024), 291-298. https://doi.org/10.21205/deufmd.2024267713.
JAMA Şenaslan İ, Bilgiç B. Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm. DEUFMD. 2024;26:291–298.
MLA Şenaslan, İbrahim ve Boğaç Bilgiç. “Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated With Genetic Algorithm”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, c. 26, sy. 77, 2024, ss. 291-8, doi:10.21205/deufmd.2024267713.
Vancouver Şenaslan İ, Bilgiç B. Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm. DEUFMD. 2024;26(77):291-8.
 Kapak Resmi İndir

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.