Comparative Analysis on the Effect of Triangular and Gaussian Membership Functions on Fuzzy Controlled Vehicle Platoon
AbstractFuzzy controlled vehicle platoon system provides a simplified yet robust approach to achieving platoon string stability and uniform inter-vehicular gap keeping in autonomous vehicle platoon. Most truck platoon is affected by delay of platoons which causes trucks not to maintain a constant inter-vehicular gap and speed, unknown uncertainties which may result in crash or accident. However, the fuzzification and defuzzification method adopted affects the final platoon characteristics of the platoon to a large extent, while also determining velocity stability timing, although researcher select a fuzzification/defuzzification method based on comfort, familiarity or simplicity. This paper proposes to compare the effect of fuzzification and defuzzification method on vehicle platoon, to provide evidence on the selection criteria and how it affects the controlled system. Simulation was done in MATLAB environment, and the fuzzy control approach was applied to a 3-vehicle autonomous platoon, which was a combination of triangular/centroid, triangular/bisector, triangular/mean with Gaussian/centroid, Gaussian/bisector and Gaussian/mean of maxima under the same platoon scenario. It was found that the best performing combination is the triangular/centroid with 4.44 secs velocity stability for vehicle V3, and 1.91 secs distance stability for follower vehicle, when compared to Gaussian/MoM with 79.88 secs velocity stability V3, and 85.89 secs for follower vehicle which is the worst performing combination.
Abdulnabi, A. (2017). PID Controller Design for Cruise Control System using Particle Swarm Optimization. Iraqi Journal for Computers Informatics (IJCI), 43(2), 30-35.
Abou-Zeid, H., Pervez, F., Adinoyi, A., Aljlayl, M., & Yanikomeroglu, H. (2019). Cellular V2X Transmission for Connected and Autonomous Vehicles Standardization, Applications, and Enabling Technologies. IEEE Consumer Electronics Magazine, 8(6), 91-98.
Ajayi, O.-o., Yahaya, S., & Umar, A. (2022). Application of Fuzzy Control System to Autonomous Vehicle Platoon. Nigerian Journal of Engineering, 29(2), 66-66.
Ajayi, O., Adebiyi, R., Abubakar, Z., Yusuf, S., Adekale, A., Abdulwahab, I., . . . Abdullahi, M. (2023). Simulation of Truck Platoon System using Anylogic Software. FUW Trends in Science & Technology Journal, 8(2), 224 – 227.
Axelsson, J. (2016). Safety in Vehicle Platooning: A Systematic Literature Review. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1033-1045. doi:10.1109/TITS.2016.2598873
Chakraverty, S., Sahoo, D. M., & Mahato, N. R. (2019). Defuzzification. In Concepts of Soft Computing (pp. 117-127). Springer, Singapore: Springer.
Elliott, D., Keen, W., & Miao, L. (2019). Recent Advances in Connected and Automated Vehicles. Journal of Traffic and Transportation Engineering (English Edition), 6(2), 109-131. doi:https://doi.org/10.1016/j.jtte.2018.09.005
Fiengo, G., Lui, D. G., Petrillo, A., Santini, S., & Tufo, M. (2019). Distributed Robust PID Control for Leader Tracking in Uncertain Connected Ground Vehicles with V2V Communication Delay. IEEE/ASME Transactions on Mechatronics, 24(3), 1153-1165. doi:10.1109/TMECH.2019.2907053
Group, B. (2020). Technical Specifications BMW 520d Efficient Dynamics Edition. Available: htt[s://www.press.bmwgrou[.com/global/.
He, D., & Peng, B. (2020). Gaussian Learning-based Fuzzy Predictive Cruise Control for Improving Safety and Economy of Connected Vehicles. IET Intelligent Transport Systems, 14(5), 346-355.
Horowitz, R., & Varaiya, P. (2000). Control Design of an Automated Highway System. Proceedings of the IEEE, 88(7), 913-925. doi:10.1109/5.871301
Li, H., Tiwari, R., Pickert, V., & Dlay, S. (2018). Fuzzy Control for Platooning Systems Based on V2V Communication. Paper presented at the 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, UK.
Li, S. E., Zheng, Y., Li, K., Wang, L.-Y., & Zhang, H. (2017). Platoon Control of Connected Vehicles from a Networked Control Perspective: Literature Review, Component Modeling, and Controller Synthesis. IEEE Transactions on Vehicular Technology, 1-1. doi:10.1109/TVT.2017.2723881
Ma, Y., Li, Z., Malekian, R., Zhang, R., Song, X., & Sotelo, M. A. (2018). Hierarchical Fuzzy Logic-based Variable Structure Control for Vehicles Platooning. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1329-1340.
Nguyen, A.-T., Taniguchi, T., Eciolaza, L., Campos, V., Palhares, R., & Sugeno, M. (2019). Fuzzy Control Systems: Past, Present and Future. IEEE Computational Intelligence Magazine, 14(1), 56-68. doi:10.1109/MCI.2018.2881644
Nishida, M., & Sugeno, M. (1985). Fuzzy Control of Model Car. Fuzzy sets systems, 60(6), 103-113.
Nowakowski, C., Shladover, S. E., Lu, X.-Y., Thompson, D., & Kailas, A. (2015). Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives.
Pappis, C. P., & Mamdani, E. H. (1977). A Fuzzy Logic Controller for a Trafc Junction. IEEE Transactions on Systems, Man, and Cybernetics, 7(10), 707-717. doi:10.1109/TSMC.1977.4309605
Saade, J. J., & Diab, H. B. (2004). Defuzzification Methods and New Techniques for Fuzzy Controllers. Iranian Journal of Electrical and Computer Engineering (IJECE), 3(2), 161-174. doi:1682-0053(04)0238
Shrivastava, S. (2019). V2V Vehicle Safety Communication. In Connected Vehicles (pp. 117-155). Springer, Cham.: Springer.
Shukla, D., Kumar, V., & Prakash, A. (2020). Performance Evaluation of IEEE 802.11 p Physical Layer for Efficient Vehicular Communication. In Advances in VLSI, communication, and signal processing (pp. 51-60). Springer, Singapore: Springer.
Singh, H., & Lone, Y. A. (2020). Fuzzy Inference Systems. In Deep Neuro-Fuzzy Systems with Python (pp. 93-127): Springer.
Sugeno, M., Murofushi, T., Mori, T., Tatematsu, T., & Tanaka, J. (1989). Fuzzy Algorithmic Control of a Model Car by Oral Instructions. Fuzzy sets systems, 32(2), 207-219. doi:https://doi.org/10.1016/0165-0114(89)90255-8
Trabia, M., Shi, L. Z., & Hodge, N. E. (2006). A fuzzy logic controller for autonomous wheeled vehicles. In Jonas Buchli, Mobile Robots, Moving Intelligence, Advanced Robotic Systems International; pro literatur Verlag, 175-200.
Xavier, P., & Pan, Y.-J. (2009). A Practical PID-Based Scheme for the Collaborative Driving of Automated Vehicles. Paper presented at the Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, Shanghai, China.
Yen, J. (1999). Fuzzy logic: Intelligence, Control, and Information. Prentice Hall Upper Saddle River, NJ, : Pearson Education India.
Zadeh, L. A. (1965). Information and Control. Fuzzy sets, 8(3), 338-353.
Ziebinski, A., Cupek, R., Grzechca, D., & Chruszczyk, L. (2017). Review of advanced driver assistance systems (ADAS). Paper presented at the AIP Conference Proceedings.
Copyright (c) 2023 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors hereby represent and warrant that the paper is original and that they are the authors of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. If in future any violation of any copyright come in notice, then the author will be responsible and not FUOYEJET.
The authors declare that:
- This paper has not been published in the same form elsewhere.
- It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
- A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, the copyright after publication belongs to the Author(s) (for articles published in 2020 and beyond) and licensed under the creative commons license CC-BY-NC (http://creativecommons.org/licenses/by-nc/4.0). The copyright covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.