Showing 3 results for Khanmirza
E. Khanmirza, H. Darvish, F. Gholami, E. Alimohammadi,
Volume 6, Issue 4 (12-2016)
Abstract
Accurate and correct performance of controller in cruise control systems is important. Hence, in such systems, controller should optimize itself against noise and probable changes in system dynamic. As a matter of fact, in this article three approaches have been conducted to-ward this purpose: MIT, direct estimation and indirect estimation. These approaches are used as controllers to track reference signal. First the performance of each of these three controllers is checked. comparison of performances indicated better behavior for indirect estimation than others. Also, it has less sensitivity against external noise. Finally, by using indirect estimation method as an adaptive control approach, two parallel separate controllers are designed for two inputs, gas and braking, and their performances are compared with recent studies. It shows improvement in performance of adaptive cruise control system to track reference signal.
Ehsan Alimohammadi, Esmaeel Khanmirza, Mr Hamed Darvish Gohari,
Volume 8, Issue 4 (12-2018)
Abstract
In cruise control systems, the performance of the controller is important. Hence, in order to have accurate results, the nonlinear behavior of a vehicle model should also be considered. In this article, a vehicle with a nonlinear model is controlled by using a nonlinear method. The nonlinear term of the model is the generated torque of engine, which is a polynomial equation. In addition, feedback linearization is used as a nonlinear method in order to design two parallel controllers to control the movement of the vehicle. These two parallel controllers are used to control braking and gas pedals which are in charge of the angular velocity of the wheels. To check the performances of controllers, first, each controller is used separately. Finally, two parallel controllers are used to track the reference signal. Comparison between results shows that the designed controller is able to reduce the convergence time of about 10 seconds. This improvement is near 35% in comparison with near studies. In addition, it can reduce the error between the velocity of the vehicle and the values of the reference signal that results in more safety for passengers.
Mr. Amid Maghsoudi, Dr. Esmaeel Khanmirza, Mr. Farshad Gholami,
Volume 10, Issue 3 (9-2020)
Abstract
Traffic control is a major and common problem in large-scale urban decision-making, particularly in metropolises. Several models of intelligent highways have been proposed to tackle the issue, and the longitudinal speed control of vehicles remains a key issue in the field of intelligent highways. Many researchers have been investigating the longitudinal speed control of vehicles. However, their proposed models disregard important and influential presumptions. In the present study, the longitudinal dynamics control of vehicles in the presence of nonlinear factors, such as air resistance, rolling resistance, a not ideal gearbox, an internal combustion engine and a torque converter, is investigated. Moreover, considering the presented model and using model reference adaptive control, a proper controller is designed to control the longitudinal speed of intelligent vehicles. The results of the proposed model, which is validated by commercial software, are in good agreement with real-world situations. Hence, a positive step is taken for controlling longitudinal speed of intelligent vehicles on an intelligent highway platform.
