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Showing 3 results for Fuzzy Logic

M. Bostanian, S. M. Barakati, B. Najjari, D. Mohebi Kalhori,
Volume 3, Issue 3 (9-2013)

Hybrid Electric Vehicles (HEVs) are driven by two energy convertors, i.e., an Internal Combustion (IC) engine and an electric machine. To make powertrain of HEV as efficient as possible, proper management of the energy elements is essential. This task is completed by HEV controller, which splits power between the IC engine and Electric Motor (EM). In this paper, a Genetic-Fuzzy control strategy is employed to control the powertrain. Genetic-Fuzzy algorithm is a method in which parameters of a Fuzzy Logic Controller (FLC) are tuned by Genetic algorithm. The main target of control is to minimize two competing objectives, consisting of energy cost and emissions, simultaneously. In addition, a new method to consider variations of Battery State of Charge (SOC) in the optimization algorithm is proposed. The controller performances are verified over Urban Dinamometer Driving Cycle (UDDS) and New Europian Driving Cycle (NEDC). The results demonstrate the effectiveness of the proposed method in reducing energy cost and emissions without sacrificing vehicle performance.
S. H. Tabatabaei Oreh, R. Kazemi, N. Esmaeili,
Volume 4, Issue 3 (9-2014)

Direct Yaw moment Control systems (DYC) can maintain the vehicle in the driver’s desired path by distributing the asymmetric longitudinal forces and the generation of the Control Yaw Moment (CYM). In order to achieve the superior control performance, intelligent usage of lateral forces is also required. The lateral wheel forces have an indirect effect on the CYM and based upon their directions, increase or decrease the amount of CYM magnitude. In this paper, a systematic and applicable algorithm is proposed to use the lateral force in the process of Yaw controlling optimally. The control systems are designed based on the proposed algorithm. This system includes Yaw rate controller and wheel slip controllers which are installed separately for each wheel. Both of the mentioned control systems are designed on the basis of the Fuzzy logic. Finally, the capabilities of the proposed control systems are evaluated in a four wheel drive vehicle, for which, the traction of each wheel can be controlled individually. It is shown that considering the lateral force effect offers significant improvement of the desired yaw rate tracking
A. Khodayari, M. Yousefi,
Volume 6, Issue 2 (6-2016)

In recent years due to improvements of technology within automobile industry, design process of advanced driver assistance systems for collision avoidance and traffic management has been investigated in both academics and industrial levels. Detection of traffic signs is an effective method to reach the mentioned aims. In this paper a new intelligent driver assistance system based on traffic sign detection with Persian context is designed. The main goal of this system is to assist drivers to choose their path based on traffic signs more precisely. To reach this purpose, a new framework by using of fuzzy logic was used for detection of traffic signs in videos which have has been captured from a vehicle path in highways. Fuzzy logic increases inference and intelligent capabilities in smart systems to make correct decision making in online conditions. Then, the combination of Maximally Stable Extermal Regions (MSER) and Canny Edge Detector Algorithms are used to detect road sign’s texts detection. MSER algorithm is aimed at assists to detect regions in an image that differ in properties, for example in brightness or color, compared to surrounding regions. Also, canny edge detector uses a multi-stage algorithm to detect a wide range of edges in the images. Thereafter, morphological mask operator is used to join individual characters for final detection of texts in the traffic signs. Finally, MATLAB Optical Character Recognition (OCR) is employed to recognize the detected texts. This new framework gives an overall text detection and recognition rate of . .

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