Fuzzy Decision Based Modeling of Rheostatic Brake System for Autonomous Land Vehicles

Abstract

The most fundamental characteristic of autonomous vehicles (AVs) is their autonomy. However, due to the dynamic operating environment of the vehicle, their control algorithms may make imprecise, approximate, and unreliable decisions. Therefore, there is a need for the creation of more robust driving algorithms, notably consistent obstacle avoidance algorithms. Occasionally, the vehicle must come to a complete stop in order to avoid obstacles. In this situation, the engine brake control of the car can be engaged. In this study, a fuzzy model was proposed to effectively brake autonomous land vehicles, with an electrical braking system known as rheostatic braking. Since a rheostatic braking system (RBS) is employed, the input values of the fuzzy controller for this designed modeling are vehicle speed and ground slipperiness, and the output value is the rheostat resistance value. In the developed fuzzy controller, Mamdani inference and Aggregation methods were utilized. In addition to these two methods, the fuzzy controller also provides the output of the centroid, bisector, average of the maximum, smallest of the maximum and largest of the maximum sharpening methods to the user. Finally, using the Python programming language and the Tkinter library, the graphical user interface displays the linguistic expression and membership degree of the user's inputs, the final fuzzy output graph, and the exact outputs from all clarification methods (GUI).

Keywords

• Fuzzy Logic
• Autonomous Vehicles
• Rheostatic Braking System

Fuzzy Decision Based Modeling of Rheostatic Brake System for Autonomous Land Vehicles

Abstract

Otonom araçların (OA'lar) en temel özelliği otonom olmalarıdır. Ancak aracın dinamik çalışma ortamı nedeniyle kontrol algoritmaları kesin olmayan, yaklaşık ve güvenilmez kararlar verebilir. Bu nedenle, özellikle tutarlı engellerden kaçınma algoritmaları olmak üzere daha sağlam sürüş algoritmalarının oluşturulmasına ihtiyaç vardır. Bazen, engellerden kaçınmak için aracın tamamen durması gerekir. Bu durumda aracın motor freni kontrolü devreye alınabilir. Bu çalışmada, reostatik frenleme olarak bilinen elektrikli fren sistemi ile otonom kara taşıtlarını etkin bir şekilde frenlemek için bulanık bir model önerilmiştir. Reostatik fren sistemi (RFS) kullanıldığı için, tasarlanan bu modelleme için bulanık denetleyicinin giriş değerleri araç hızı ve zemin kayganlığı, çıkış değeri ise reostat direnç değeridir. Geliştirilen bulanık denetleyicide Mamdani çıkarımı ve Toplama yöntemleri kullanılmıştır. Bu iki yönteme ek olarak, bulanık denetleyici ayrıca kullanıcıya ağırlık merkezi, açıortay, maksimumun ortalaması, maksimumun en küçüğü ve maksimumun en büyüğü keskinleştirme yöntemlerinin çıktısını verir. Son olarak, Python programlama dilini ve Tkinter kitaplığını kullanarak, grafik kullanıcı arayüzü, kullanıcının girdilerinin dilsel ifadesini ve üyelik derecesini, nihai bulanık çıktı grafiğini ve tüm durulaştırma yöntemlerinden (GUI) kesin çıktıları görüntüler.

Keywords

• Bulanık Mantık
• Otonom Araçlar
• Reostatik Frenleme Sistemi

References

1. Bao, D. Q., & Zelinka, I. (2019). Obstacle avoidance for swarm robot based on self-organizing migrating algorithm. Procedia Computer Science, 150, 425–432.
2. Castaneda, M. A. P., Savage, J., Hernandez, A., & Cosío, F. A. (2008). Local autonomous robot navigation using potential fields. In Motion Planning. IntechOpen.
3. Chen, X., & Li, Y. (2006). Smooth path planning of a mobile robot using stochastic particle swarm optimization. 2006 International Conference on Mechatronics and Automation, 1722–1727.
4. Chengqing, L., Ang, M. H., Krishnan, H., & Yong, L. S. (2000). Virtual obstacle concept for local-minimum-recovery in potential-field based navigation. Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2, 983–988.
5. Engedy, I., & Horváth, G. (2009). Artificial neural network based mobile robot navigation. 2009 IEEE International Symposium on Intelligent Signal Processing, 241–246.
6. Godfrey, A. J., & Sankaranarayanan, V. (2018). A new electric braking system with energy regeneration for a BLDC motor driven electric vehicle. Engineering Science and Technology, an International Journal, 21(4), 704–713.
7. Günay, M., Korkmaz, M. E., & Özmen, R. (2020). An investigation on braking systems used in railway vehicles. Engineering Science and Technology, an International Journal, 23(2), 421–431.
8. Kadlec, P., & Raida, Z. (2011). A Novel Multi-Objective Self-Organizing Migrating Algorithm. Radioengineering, 20(4).

9. Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. ICRA, 2, 1398–1404.
10. Lee, M. C., & Park, M. G. (2003). Artificial potential field based path planning for mobile robots using a virtual obstacle concept. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), 2, 735–740.
11. Pan, C., Chen, L., Chen, L., Jiang, H., Li, Z., & Wang, S. (2016). Research on motor rotational speed measurement in regenerative braking system of electric vehicle. Mechanical Systems and Signal Processing, 66, 829–839.
12. Sezer, V., & Gokasan, M. (2012). A novel obstacle avoidance algorithm:“Follow the Gap Method.” Robotics and Autonomous Systems, 60(9), 1123–1134.
13. Shimoda, S., Kuroda, Y., & Iagnemma, K. (2005). Potential field navigation of high speed unmanned ground vehicles on uneven terrain. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2828–2833.
14. Tu, J., & Yang, S. X. (2003). Genetic algorithm based path planning for a mobile robot. 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), 1, 1221–1226.
15. Xu, W., Chen, H., Zhao, H., & Ren, B. (2019). Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system. Mechatronics, 57, 95–108.
16. Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83–93.
17. Zelinka, I. (2004). SOMA—self-organizing migrating algorithm. In New optimization techniques in engineering (pp. 167–217). Springer.
18. Zelinka, I., & Lampinen, J. (1999). An evolutionary learning algorithms for neural networks. Proceedings of the 5th International Conference on Soft Computing, 410–414.