Behavior Evaluation of Vehicle Platoon via Different Fuzzy-X Tuned Controllers

• Published in: 2020 8th International Conference on Control, Mechatronics and Automation (ICCMA) pp. 149 - 155
• Main Authors: Olwan, Mohamed A., Mostafa, AbdELRahman A., AbdELAty, Youssef M., Mahfouz, Dalia M., Shehata, Omar M., Morgan, Elsayed I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.11.2020
• Subjects: Drag; Fuzzy Logic Control (FLC); Genetic Algorithm (GA); Genetic algorithms; Mathematical model; Neural Network (NN); Proportional-Integral-Derivative (PID); Roads; Torque; Tuning; Vehicle dynamics; Vehicle platoon
• DOI: 10.1109/ICCMA51325.2020.9301503

Over the past few decades, Intelligent Transportation System (ITS) has become a topic of considerable interest for transportation development. One of the significant applications of ITS is vehicle platooning, which is described as a string of fully or partly automated vehicles traveling together at closely controlled inter-vehicular distances while preserving a set speed without violating safety restrictions. Control studies of a platoon of vehicles moving in a single dimension has recently attracted extensive research interest. The platoon control challenge relies on the adaptation of the predefined vehicle's speed while keeping a safe gap between each two successive vehicles. This study investigates intelligent unidirectional decentralized control approach based on Fuzzy Logic Control (FLC). The performance of the controller is tuned using three different techniques through the hybridization of FLC with two approaches; Genetic Algorithm (GA) and Proportional-Integral-Derivative (PID) and the adaptation of FLC with Neural Networks (NN) forming Fuzzy-X tuned controllers to control the follower vehicles to achieve their objective. In order to accomplish the goal of the study, each vehicle is represented through a longitudinal vehicle dynamic model. A reference velocity trajectory is built for the vehicles to follow. Simulations are conducted to evaluate the performance of each controller in terms of spacing error convergence and desired velocity tracking. Results show that the desired tracking performance and gap control are achieved by all the controllers with certain limitations that are discussed through out the study.