Modeling a Tracked Mobile Robot and Trajectory Control by Using Fuzzy Logic

Yıl 2020, Cilt: 35 Sayı: 2, 357 - 368, 30.06.2020

Osman Doğmuş Mahit Güneş

https://doi.org/10.21605/cukurovaummfd.792422

Öz

In this study, a tracked autonomous vehicle was designed and the trajectory control of the designed autonomous vehicle has been carried out using a fuzzy logic controller (FLC). The multi-purpose autonomous vehicle has the features of automatic steering, performing a specific task, carrying loads to various environments. Different control techniques are used in the trajectory control of autonomous vehicles. The performance of the orbital control structure of the autonomous robot has been compared with the classical PI controller and simulation studies were realized by using the control system Matlab/Simulink model. It has been demonstrated by simulation studies that BMD increases the performance of the system and provides a more stable structure.

Anahtar Kelimeler

Tracked mobile robot, Dynamic modeling, Kinematic modeling, PI, FLC

Paletli Bir Mobil Robotun Modellenmesi ve Bulanık Mantık ile Yörünge Kontrolü

Öz

Bu çalışmada, paletli otonom bir araç tasarlanmış olup tasarlanan otonom aracın yörünge kontrolü bulanık mantık denetleyici (BMD) kullanılarak gerçekleştirilmiştir. Çok amaçlı tasarlanan otonom araç, otomatik yönlendirme, belirli bir görevi icra etme, çeşitli ortamlara yük taşıyabilme özelliklerine sahiptir.
Otonom araçların yörünge kontrolünde farklı kontrol teknikleri kullanılmaktadır. Otonom robotun yörünge kontrol yapısının performansını klasik PI denetleyici ile karşılaştırılmış ve kontrol sistemi Matlab/Simulink modeli kullanılarak benzetim çalışmaları gerçekleştirilmiştir. BMD’nin sistemin performansını artırdığı ve daha kararlı bir yapı sağladığı gerçekleştirilen benzetim çalışmaları ile ortaya konulmuştur.

Anahtar Kelimeler

Paletli mobil araç, Dinamik modelleme, Kinematik modelleme, PI, BMD

Kaynakça

• 1. Dong, H., Luo, Z., 2011. Control Strategies of Human Interactive Robot Under Uncertain Environments. Mobile Robots: Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training (2nd Edition). Intech, Croatia, p. 390. https://doi.org/10.5772/2304.
• 2. Blasko, V., Kaura, V., 1997. A New Mathematical Model and Control of a Three- phase AC-DC Voltage Source Converter. IEEE Transactions on Power Electronics, 12(1), 116-123.
• 3. Kececioglu, F.O., Acikgoz, H., Yildiz, C., Gani, A., Sekkeli, M., 2017. Power Quality Improvement Using Hybrid Passive Filter Configuration for Wind Energy Systems. Journal of Electrical Engineering and Technology, 12(1), 207–216.
• 4. Eltamaly, A.M., Alolah, A.I., Badr, B.M., 2010. Fuzzy Controller for Three Phases Induction Motor Drives. IEEE 2010 International Conference on Autonomous and Intelligent Systems, 1–6.
• 5. Kılıç, E., Özçalık, H.R., Şit, S., 2018. Adaptive Controller with RBF Neural Network for Induction Motor Drive. International Journal of Numerical Modelling. Electronic Networks, Devices and Fields, 31(3), 1–11.
• 6. Şit, S., Özçalik, H.R., Kılıç, E., Doğmuş, O., 2016. Asenkron Motorların Online Adaptif Sinirsel-Bulanık Denetim (ANFIS) Sistemine Dayalı Hız Denetim Performansının İncelenmesi. Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 31(ÖS 2), 33–41.
• 7. Sidi, M.H.A., Hudha, K., Kadir, Z.A., Amer, N.H., 2018. Modeling and Path Tracking Control of a Tracked Mobile Robot. 2018 IEEE 14th International Colloquium on Signal Processing and its Application, 9-10 March, 72–76.
• 8. Huang, P., Zhang, Z., Luo, X., Zhang, J., Huang, P., 2018. Path Tracking Control of a Differential-Drive Tracked Robot Based on Look-ahead Distance. IFAC-Papers On Line, 51(17), 112–117.
• 9. Asai, M., Chen, G., Takami, I., 2019. Neural Network Trajectory Tracking of Tracked Mobile Robot. 16th International Multi- Conference on Systems, Signals and Devices, (4), 225–230.
• 10. J i, P., Li, S., Xu, M., Li, J., Guo, J., 2018. Design of Sliding Cloud-Model Cross Coupling Controller for Tracked Mobile Robot. Proceedings of the 37th Chinese Control Conference, July 25-27, 5353–5357.
• 11. Jayakumar, V., Kumar, M., 2012. Engineering Mechanics. New Delhi: PHI Learning Private Limited.
• 12. Malu, S.K., Majumdar, J., 2014. Kinematics, Localization and Control of Differential Drive Mobile Robot. Global Journal of Researches in Engineering: Robotics & Nano-Tech, 14(1), 1-9.
• 13. Gholipour, A., Yazdanpanah, M.J., 2003. Dynamic Tracking Control of Nonholonomic Mobile Robot with Model Reference Adaptation for Uncertain Parameters. 2003 European Control Conference, 3118–3122.
• 14. Wu, X., Xu, M., Wang, L., 2013. Differential Speed Steering Control for Four-wheel Independent Driving Electric Vehicle. IEEE International Symposium on Industrial Electronics, 1(4), 355-359.
• 15. Ogata, K., 2010. Modern Control Engineering (5th Edition). Prentice Hall, New Jersey, 905.
• 16. Kayacan, E., Khanesar, M.A., 2015. Fuzzy Neural Networks For Real Tıme Control Applıcatıons (1st Edition). Butterworth Heinemann, Boston, 264. https://doi.org/10. 1016/C2014-0-02444-6
• 17. Antao, R., 2017. Type-2 Fuzzy Logic Uncertain Systems Modeling and Control. Higher Education Press, Beijing, 136. https://doi.org/ 10.1007/978-981-10-4633-9.
• 18. Ocampo-duque, W., Osorio, C., Piamba, C., Schuhmacher, M., Domingo, J.L. 2013. Water Quality Analysis in Rivers with Non- parametric Probability Distributions and Fuzzy Inference Systems: Application to the Cauca River , Colombia. Environment International, 52, 17–28.
• 19. Aisbett, J., Rickard, J.T., 2014. Centroids of Type-1 and Type-2 Fuzzy Sets When Membership Functions Have Spikes. IEEE Transactions on Fuzzy Systems, 22(3), 685-692.