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Showing 6 results for Kazemi

S. H. Tabatabaei Oreh, R. Kazemi, N. Esmaeili,
Volume 4, Issue 3 (9-2014)
Abstract

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
R. Kazemi, M. Abdollahzade,
Volume 5, Issue 1 (3-2015)
Abstract

Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree learning algorithm in offline mode, which produces favorable extrapolation performance, and then, is adapted to the stream of car following data, e.g. velocity and acceleration of the target vehicle, using an adaptive least squares estimation. The proposed approach is validated by means of real-world car following data sets. Simulation results confirm the satisfactory performance of the OFNN for adaptive car following modeling application.

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