TEKERLEK İÇİ MOTORLU ELEKTRİKLİ BİR ARACIN AKTİF SÜSPANSİYON SİSTEMİNİN PID VE BULANIK MANTIK TABANLI KONTROLÜ

Yıl 2020, Cilt: 25 Sayı: 3, 1373 - 1390, 31.12.2020

Mustafa Tayyip Toksoy , Ahmet Yıldız

https://doi.org/10.17482/uumfd.789019

Cited By: 1

Öz

Bu çalışmada tekerlek içi motorlu elektrikli bir aracın aktif süspansiyon sistemleri PID ve Bulanık Mantık denetleyicileri ile kontrol edilmiş ve performansları karşılaştırılmıştır. Doğrusal olmayan süspansiyon yaylarının kullanıldığı ve sürücü modeli ile birlikte 14 serbestlik derecesine sahip modelin hareket denklemleri çıkarılarak Matlab/Simulink ortamında çözdürülmüştür. Daha sonra PID ve Bulanık Mantık yöntemleri yardımı ile tam taşıt modelinde aktif süspansiyon sistemlerinin kontrolü gerçekleştirilmiş ve elde edilen sonuçları kontrolcü olmadan çıkan simülasyon sonuçları ile karşılaştırılmıştır. Çalışmada kontrolcü tasarımına yönelik Bulanık Mantık yönteminin hesaplama metodu, buna ait hata oranı ve hata oranının grafikleri, PID kontrolcü tasarımı, hesaplamaları ve sonuçları sunulmuştur. Sonuçta, her iki kontrolcü ile elde edilen verilerin kontrolcü olmadan elde edilenlere göre titreşim genliğini büyük oranda düşürdüğü ve ayrıca Bulanık Mantık ile yapılan kontrolün daha iyi sonuçlar verdiği gözlemlenmiştir.

Anahtar Kelimeler

PID, Bulanık, Mantık, Elektirkli, Araç, Süspansiyon

PID and Fuzzy Logic Based Control of the Active Suspension System of an Electric Vehicle with In- Wheel Motor

Öz

In this study, active suspension systems of an electric vehicle with in-wheel motor are controlled with PID and Fuzzy Logic controllers and their performances are compared. The equations of motions are drived by using the 14 degrees of freedom model with nonlinear suspension springs and the driver model and solved in Matlab / Simulink environment. Then, control of active suspension systems in full vehicle model is performed with the help of PID and Fuzzy Logic methods and the obtained results are compared with the simulation outcomes without a controller. The calculation method of the Fuzzy Logic method for controller design, the related error rate and error rate graphs, PID controller design, calculations and results are presented. As a result, it is observed that the data obtained with both controllers decrease the vibration amplitude significantly compared to the ones obtained without the controller and also the control performed with Fuzzy Logic gives better results.

Anahtar Kelimeler

PID, Fuzzy, Logic, Electrical, Vehicle, Suspension

Kaynakça

• 1. Allen, J.A. (2008) Design of active suspension control based upon use of tubular linear motor and quarter-car model, Master Thesis, Texas A&M University, Texas.
• 2. Badran, S., Salah, A., Abbas, W., Abouelatta, O.B. (2012) Design of Optimal Linear Suspension for Quarter Car with Human Model using Genetic Algorithms, The Research Bulletin of Jordan ACM, 2(2), 42-51.
• 3. Cao, D., Song, X., Ahmadian, M. (2011) Editors’ perspectives: road vehicle suspension design, dynamics, and control, Vehicle System Dynamics, 49(1-2), 3-28. doi: 10.1080/00423114.2010.532223
• 4. Doğan, H., Kaplan, K., Kuncan, M., Ertunç, H.M. (2015) Araç Süspansiyon Sistemi Kontrolüne PID ve Bulanık Mantık Yaklaşımları, Otomatik Kontrol Ulusal Toplantısı, Denizli, 699-704.
• 5. Gillespie, T.D. (1996) Fundamentals of Vehicle Dynamics, SAE International, Warrendale.
• 6. Güçlü, R. (2005) Fuzzy Logic Control of Seat Vibrations of a Non-Linear Full Vehicle Model, Nonlinear Dynamics, 40(2005), 21–34. doi: 10.1007/s11071-005-3815-7
• 7. Jazar, R. N. (2017) Vehicle Dynamics: Theory and Application, Springer International, New York.
• 8. Jin, L., Yu, Y., Fu, Y. (2016) Study on the ride comfort of vehicles driven by in-wheel motors, Advanced Mechanical Engineering, 8(3), 1–9. doi: 10.1177/1687814016633622
• 9. Li, H., Jing, X., Karimi, H.R. (2014) Output-feedback-based H∞ control for vehicle suspension systems with control delay, IEEE Transactions on Industry Applications, 61(1), 436–46. doi: 10.1109/TIE.2013.2242418
• 10. Mehdizadeh, S. A. (2015) Optimization of passive tractor cabin suspension system using ES, PSO and BA, Journal of Agricultural Technology, 11(3), 595-607.
• 11. Nikam, S., Vandana, R., Fernandes, B. A. (2012) High-Torque-Density Permanent-Magnet Free Motor for In-Wheel Electric Vehicle Application, IEEE Transactions on Industry Applications, 48(6), 2287–2295. doi: 10.1109/TIA.2012.2227053
• 12. Oliveira, K., Cesar, M., Goncalves, J. (2017) Fuzzy based Control of a Vehicle Suspension System using a MR Damper, Proceedings of the 12th Portuguese Conference on Automatic Control, Guimaraes, 14–16.
• 13. Özkop, E., Altaş, İ.H. (2007) Bulanık Mantık Denetleyici ile Aktif Otomobil Süspansiyon Denetimi, 12. Elektrik Elektronik Bilgisayar Biyomedikal Mühendisliği Ulusal Kongresi ve Fuarı, Eskişehir,14–18.
• 14. Rao, M.V.C., Prahlad, V. (1997) A tunable fuzzy logic controller for vehicle-active suspension systems, Fuzzy Sets and Systems, 85(1), 11-21. doi: 10.1016/0165- 0114(95)00369-X
• 15. Shirahatt, A., Prasad, P.S.S., Panzade, P., Kulkarni, M.M. (2008) Optimal Design of Passenger Car Suspension for Ride and Road Holding, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 30(1), 67-68. doi: 10.1590/S1678- 58782008000100010
• 16. Sun, W., Pan, H., Yu, J., Gao, H. (2014) Reliability control for uncertain half-car active suspension systems with possible actuator faults, IET Control Theory & Applications, 8(9), 746-754. doi: 10.1049/iet-cta.2013.0471
• 17. Takagi, T., Sugeno, M. (1985) Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116-132. doi: 10.1109/TSMC.1985.6313399
• 18. Takahashi, T., Takemoto, M., Ogasawara, S., Hino, W., Takezaki, K.S. (2017) Size and weight Reduction of an In-wheel Axial-gap Motor Using Ferrite Permanent Magnets for Electric Commuter Cars, IEEE Transactions on Industry Applications, 53(4), 3927-3935. doi: 10.1109/TIA.2017.2684739.
• 19. Yokoyama, M., Hedrick, J.K., and Toyama, S. (2001) A model following sliding mode controller for semi-active suspension systems with MR dampers. Proceedings of the American Control Conference, Arlington, 25–27.
• 20. Yıldız, A. (2019a) Optimum suspension design for non-linear half vehicle model using particle swarm optimization (PSO) algorithm, The 41st International JVE Conference Vibration, Leipzig, 43-48. doi: 10.21595/vp.2019.21012
• 21. Yıldız, A. (2019b) A comparative study on the optimal non-linear seat and suspension design for an electric vehicle using different population-based optimisation algorithms, International Journal of Vehicle Design, 80(2/3/4), 241-256, doi: 10.1504/IJVD.2019.10031168.