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    Genetic PI based model and path tracking control of four traction electrical vehicle
    (Springer, 2020) Dogan, Muhsin Ugur; Guvenc, Ugur; Elmas, Cetin
    Modeling and control of four-wheel electric vehicles are difficult due to their dynamic parameters and variable road conditions. In this paper, a robust and adaptive electric vehicle model and position control that can be adapted to state variables using a dynamic lateral and longitudinal model of a four-wheel electric vehicle have been proposed. The longitudinal and lateral forces have been modeled according to Newton's second law, depending on the parameters such as the vehicle's size, width, height, weight and slope angle by using dynamic equations of the vehicle. In this paper, a permanent magnet synchronous hub motor has been used for each wheel of the electric vehicle. The magic formula wheel model has been used to determine the relationship between the slip and the friction of the designed vehicle. Using the slip system, the relationship between the speed of the electric vehicle itself and the wheel speeds have been defined. The proportional controller at the position loop and proportional + integral controller at the speed loop of the designed system have been used. In the path tracking control system, position controls have been made in the X and Y coordinate planes. A P position controller and a PI speed controller have been used for each plane. Thus, there are 6 controller coefficients in total. Because of the complicated structure of the system, it is difficult to determine the most suitable controller coefficients by analytical methods. Therefore, the genetic algorithm which is one of the heuristic algorithms has been used in determining these coefficients. Simulation studies have been conducted with a different path and position references to see the effectiveness of the proposed electric vehicle model and position control. The obtained results show that the proposed model and control system are robust, effective and reliable.