Automated reasoning of vehicle brake-force: a fuzzy inference system model. (4th September 2022)

• Record Type: Journal Article
• Authors: Akhtaruzzaman, M.; Hossain, Md. Arman; Rahman, Md. Mahbubur; Hasan, Mohammad Kamrul
• Is Part Of: International journal of reasoning-based intelligent systems. Volume 14:Number 2/3(2022)
• Page Start: 104
• Page End: 113
• Publication Date: 2022-09-04
• Subjects: fuzzy logic -- brake-force reasoning -- fuzzy inference system -- FIS -- defuzzification methods -- Mamdani FIS -- MFIS; Artificial intelligence -- Periodicals; Reasoning -- Periodicals; 006.3
• Languages: English
• ISSNs: 1755-0556

Abstract

Automatic regulation of vehicle braking system is very crucial to control vehicle speed, keep it on its track, and avoid any collision. A traditional binary control for brake system is not appropriate as the on-off conditions induce unpleasant feelings to the passengers. Control engineering technique may solve this problem but does not reflect artificial intelligence in terms of human reasoning behaviour. Although, a few study on fuzzy-based automated brake-force reasoning has been conducted showing the superiority over all the techniques, comparisons among various defuzzification methods are rarely found in the literature. Thus, the objective of this study is to design a fuzzy-based architecture for automated brake-force reasoning system through comparative analysis among various defuzzification approaches. The model takes two inputs as linguistic variables, the speed and the vehicular distance, and the output of the system is the required brake-force. The Mamdani fuzzy inference system (MFIS) is applied in designing the fuzzy architecture for automated brake-force prediction. The output fuzzy set is defuzzified with five defuzzification methods and analysed through observations. The results manifest that out of the five defuzzification approaches, the centroid and bisector methods demonstrate most satisfactory outputs and could be chosen for real system application.