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Journal of Electronic Systems
 

Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System
Dwi Setiady, Oyas Wahyunggoro, Prapto Nugroho
Department of Electrical Engineering and Information Technology Bulaksumur, Caturtunggal, Kec. Depok, Kabupaten Sleman Daerah Istimewa Yogyakarta, Yogyakarta, 55281 Indonesia
Abstract: The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver’s experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident’s impact. The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down. There is uncertainty in determining membership function of distance and velocity. It is difficult to determine an exact membership function. In this paper, the system used Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information. The result is that the pressure on the brake pads is gradually increased with an average increase of 5.6%. At a distance of 34 m, the pressure is at maximum value.
Keywords: Artificial Intelligent System, Automatic Braking System, Type-2 Fuzzy Logic Control, Fuzzy Logic Control Design of Automatic Braking System Using Interval Type-2 Fuzzy Logic System
DOI:https://doi.org/10.6025/jes/2019/9/2/35-43
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References:[1] Korlantas-irsms.info. (2018). KORLANTAS POLRI - Accident Reports by Polda. [online] Available at: http://www.korlantas irsms.info/graph/accidentTypeTable [Accessed 1 Nov. 2018]. [2] Hirulkar, S., Damle, M., Rathee, V., Hardas, B. (2014). Design of Automatic Car Breaking System Using Fuzzy Logic and PID Controller. 2014 International Conference on Electronic Systems, Signal Processing, and Computing Technologies. [3] Jin, P., Kim, J., Kim, J. (2015). Design of unmanned vehicle advanced braking system using smart motor. 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). [4] Chen, X., Zhang, J., Liu, Y. (2016). Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System. Mathematical Problems in Engineering, 2016, p 1-12. [5] Rizianiza, I., Djafar, A. (2017). Design car braking system using Mamdani Fuzzy Logic Control. 2017 4th International Conference on Electric Vehicular Technology (ICEVT). [6] Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8 (3), 199-249. [7] Naik, K., Gupta, C. (2017). Performance comparison of Type-1 and Type-2 fuzzy logic systems. In: 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). [8] Castillo, O. (2012). Type-2 Fuzzy Logic in Intelligent Control Applications. Berlin, Heidelberg: Springer Berlin Heidelberg. [9] Karnik, N., Mendel, J. (n.d.). Introduction to type-2 fuzzy logic systems. 1998 IEEE International Conference on Fuzzy Systems, In: Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228). [10] Castillo, O. , Melin, P. (2012). Recent advances in interval type-2 fuzzy systems. Heidelberg: Springer. [11] Saxena, V., Yadala, N., Chourasia, R. Rhee, F. (2017). Type reduction techniques for two-dimensional interval type-2 fuzzy sets. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). [12] Wu, D., Mendel, J. M. (2014). Designing practical interval type-2 fuzzy logic systems made simple, In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014. [13] Taskin, A., Kumbasar, T. (2015). An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems. In: 2015 IEEE Symposium Series on Computational Intelligence. [14] Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (2007). Type-2 Fuzzy Logic: Theory and Applications. In: 2007 IEEE International Conference on Granular Computing (GRC 2007). [15] Sajiah, A., Setiawan, N., Wahyunggoro, O. (2016). Interval type-2 fuzzy logic system for diagnosis coronary artery disease. Communications in Science and Technology, 1(2) 1-55. [16] Wu, D. (2013). A Brief Tutorial on Interval Type-2 Fuzzy Sets and Systems. p.1-13.


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