Showing 46 results for Control
Mr. Hamid Rahmanei, Dr. Abbas Aliabadi, Prof. Ali Ghaffari, Prof. Shahram Azadi,
Volume 13, Issue 2 (6-2023)
Abstract
The coordinated control of autonomous electric vehicles with in-wheel motors is classified as over-actuated control problems requiring a precise control allocation strategy. This paper addresses the trajectory tracking problem of autonomous electric vehicles equipped with four independent in-wheel motors and active front steering. Unlike other available methods presenting optimization formulation to handle the redundancy, in this paper, the constraints have been applied directly using the kinematic relations of each wheel. Four separate sliding mode controllers are designed in such a way that they ensure the convergence of tracking errors, in addition to incorporating the parametric and modeling uncertainties. The lateral controller is also designed to determine the front steering angles to eliminate lateral tracking errors. To appraise the performance of the proposed control strategy, a co-simulation is carried out in MATLAB/Simulink and Carsim software. The results show that the proposed control strategy has enabled the vehicle to follow the reference path and has converged the errors of longitudinal and lateral positions, velocity, heading angle, and yaw rate. Furthermore, the proposed control system shows promising results in the presence of uncertainties including the mass and moment of inertia, friction coefficient, and the wind disturbances.
Dr. Abbas Soltani, Mr. Milad Arianfard,
Volume 13, Issue 2 (6-2023)
Abstract
In this study, an adaptive sliding mode controller (ASMC) based on estimation of tire-road friction coefficient is proposed for engagement control of automotive dry clutch. The control of clutch engagement is one of the most important parts of gear-shift process for automated manual transmission. Accurate amount of drive shaft torque in modelling of powertrain system is essential to guarantee smooth engagement of the clutch and rapid response of the control system. As the tire-road friction coefficient has significant influence on drive shaft torque, an estimator is designed to calculate this parameter. The ASMC is proposed for the clutch control to overcome the system uncertainties and a proportional integral (PI) controller is adopted to engine speed control. In addition, a nonlinear estimator utilizing unscented Kalman filter is applied to estimate the state variables that are measured hardly such as wheel slip and longitudinal vehicle velocity. The simulation results demonstrate the high effectiveness of the combined use of presented controller and road friction coefficient estimator for improving the smooth clutch engagement in comparison to the control system without estimator.
Dr Hossein Chehardoli,
Volume 13, Issue 3 (9-2023)
Abstract
In this article, the optimal robust H2 / H∞ control of self-driving car platoons (SDCPs) under external disturbance is investigated. By considering the engine dynamics and the effects of external disturbance, a linear dynamical model is presented to define the motion of each self-driving car (SDC). Each following SDC is in direct communication with the leader. By utilizing the relative position of following SDCs and the leader, the error dynamics of each SDC is calculated. The particle swarm optimization (PSO) method is utilized to find the optimal control gains. To this aim, a cost function which is a linear combination of H2 and H∞ norms of the transfer function between disturbance and target variables is constructed. By employing the PSO method, the cost function will be minimized and therefore, the robustness of the controller against external disturbance is guaranteed. It will be proved that under the presented robust control method, the negative effects of disturbance on system performance will significantly reduce. Therefore, the SDCP is internally stable and subsequently, each SDC tracks the motion of the leader. In order to validate the proposed method, simulation examples will be presented and analyzed.
Mr. Hosein Hamidi Rad, Prof. Mohsen Esfahanian, Prof. Saeed Behbahani,
Volume 13, Issue 3 (9-2023)
Abstract
This study examines the impact of a fuzzy logic-based control strategy on managing peak power consumption in the auxiliary power unit (APU) of a hybrid electric bus. The APU comprises three components: an air compressor, a power steering system, and an air conditioning system (AC) connected to an electric motor. Initially, these components were simulated in MATLAB-SIMULINK software. While the first two were deemed dependent and independent of vehicle speed, respectively, the stochastic behavior of the steering was emulated using the Monte Carlo method. Subsequently, a fuzzy controller was designed and incorporated into the APU to prevent simultaneous operation of the three accessories as much as possible. The results of repeated simulations demonstrated that the designed fuzzy controller effectively distributed the operation of the accessories throughout the driving cycle, thereby reducing overlaps in auxiliary loads. Consequently, the APU's average and maximum power consumption exhibited significant reductions. Furthermore, through multiple simulations with an upgraded power system model integrating the new APU-controller package, it was established that the proposed strategy for managing auxiliary loads in the bus led to lower fuel consumption and emissions.
Dr Mohammad H. Shojaeefard, Dr Mollajafari Morteza, Mr Seyed Hamid R. Mousavitabar,
Volume 14, Issue 1 (3-2024)
Abstract
Fleet routing is one of the basic solutions to meet the good demand of customers in which decisions are made based on the limitations of product supply warehouses, time limits for sending orders, variety of products and the capacity of fleet vehicles. Although valuable efforts have been made so far in modeling and solving the fleet routing problem, there is still a need for new solutions to further make the model more realistic. In most research, the goal is to reach the shortest distance to supply the desired products. Time window restrictions are also applied with the aim of reducing product delivery time. In this paper, issues such as customers' need for multiple products, limited warehouses in terms of the type and number of products that can be offered, and also the uncertainty about handling a customer's request or the possibility of canceling a customer order are considered. We used the random model method to deal with the uncertainty of customer demand. A fuzzy clustering method was also proposed for customer grouping. The final model is an integer linear optimization model that is solved with the powerful tools of Mosek and Yalmip. Based on the simulation results, it was identified to what extent possible and accidental changes in customer behavior could affect shipping costs. It was also determined based on these results that the effective parameters in product distribution, such as vehicle speed, can be effective in the face of uncertainty in customer demand.
Mr Seyed Amir Mohammad Managheb, Mr Hamid Rahmanei, Dr Ali Ghaffari,
Volume 14, Issue 1 (3-2024)
Abstract
The turn-around task is one of the challenging maneuvers in automated driving which requires intricate decision making, planning and control, concomitantly. During automatic turn-around maneuver, the path curvature is too large which makes the constraints of the system severely restrain the path tracking performance. This paper highlights the path planning and control design for single and multi-point turn of autonomous vehicles. The preliminaries of the turn-around task including environment, vehicle modeling, and equipment are described. Then, a predictive approach is proposed for planning and control of the vehicle. In this approach, by taking the observation of the road and vehicle conditions into account and considering the actuator constraints in cost function, a decision is made regarding the minimum number of steering to execute turn-around. The constraints are imposed on the speed, steering angle, and their rates. Moreover, the collision avoidance with road boundaries is developed based on the GJK algorithm. According to the simulation results, the proposed system adopts the minimum number of appropriate steering commands while incorporating the constraints of the actuators and avoiding collisions. The findings demonstrate the good performance of the proposed approach in both path design and tracking for single- and multi-point turns.
