Showing 2 results for Fuzzy System
A. Khodayari,
Volume 5, Issue 2 (6-2015)
Abstract
Due to the increasing demand for traveling in public transportation systems and increasing traffic of vehicles, nowadays vehicles are getting to be intelligent to increase safety, reduce the probability of accident and also financial costs. Therefore, today, most vehicles are equipped with multiple safety control and vehicle navigation systems. In the process of developing such systems, simulation has become a cost-effective chance for the fast evolution of computational modeling techniques. The most popular microscopic traffic flow model is car following models which are increasingly being used by transportation experts to evaluate new Intelligent Transportation System (ITS) applications. The control of car following is essential to its safety and its operational efficiency. This paper presents a car-following control system that was developed using a fuzzy model predictive control (FMPC). This system was used to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU) and was developed based on a new idea to calculate and estimate the instantaneous reaction of a DVU. At the end, for experimental evaluation, the FMPC system was used along with a human driver in a driving simulator. The results showed that the FMPC has better performance in keeping the safe distance in comparison with real data of human drivers behaviors. The proposed model can be recruited in driver assistant devices, safe distance keeping observers, collision prevention systems and other ITS applications.
Prof. Mohammad Javad Mahmoodabadi, Dr. Abolfazl Ansarian, Dr. Tayebeh Zohari,
Volume 15, Issue 3 (9-2025)
Abstract
This research proposes a robust fuzzy adaptive fractional-order proportional-integral-derivative (PID) controller for an active suspension system of a quarter-car model. For this, the research first designed the PID controller using chassis acceleration and relative displacement. Next, it utilized the chain derivative rule and the gradient descent mechanism to formulate adaptation rules based on integral sliding surfaces. In the next step, the control parameters were regulated by employing a fuzzy system comprising the product inference engine, singleton fuzzifier, and center average defuzzifier. Eventually, the optimum gains of the proposed controller were determined by running a multi-objective material generation algorithm (MOMGA). Simulation results implied the superiority of the proposed controller over other controllers in handling road irregularities.
