Zhang and Kim (2008) presented an AFS controller that was designed by application of the Quantitative Feedback control Theory (QFT), and was based on the integration of feedback signal from the yaw rate sensor.There are several control methods in the literature for tracking the desired response with a DYC system (e.g.
Vertical dynamic interactions between railway tracks and vehicles has been simulated in (Zakeri and et al (2009)), which calculated deflections, accelerations and forces in various track components, and also an study how parameters such as train speed, axle load, rail corrugations, wheel flats and so on influence the track and vehicle components. DYC, however, can keep the vehicle stable in critical situations where the tire cornering forces reach saturation (e.g.
4WS and AFS can effectively improve the steerability performance in the linear region of the tire (Hwang et al., 2008). The proposed control system consisted of an inner-loop controller and an outer-loop tracking controller to achieve control performance and stability.An important issue in chassis control systems is to control the vehicle yaw moment by controlling the lateral vehicle motion variables such as yaw rate and side-slip angle (Tchamna and Youn, 2013). Nam and Fujimoto (2012) proposed a robust yaw stability control design based on active front steering (AFS) control for in-wheel-motored electric vehicles with a Steer-by-Wire (SbW) system. In order to improve vehicle stability and steerability, electronic control systems such as direct yaw moment control (DYC), electronic stability program (ESP), active steering including 2 Wheel Steering (2WS) and 4 Wheel Steering (4WS), or active front steering (AFS) have been developed).
#Sharp driver optimal control model drivers
Remarkable advances achieved in the automotive active safety systems during the recent decades by means of active control and assist drivers to maintain the control of vehicles have resulted in preventing unintended vehicle behavior. Keywords: driver model, vehicle path following, ADAMS, PID controller and genetic algorithm IIDepartment of Mechanical Engineering, Parand Branch, Islamic Azad University, Parand, Tehran, Iran of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran ISchool of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran Simulation results show the pronounced effectiveness of the controller in vehicle path following and stability.ĭriver model vehicle path following ADAMS PID controller and genetic algorithmĪ path-following driver/vehicle model with optimized lateral dynamic controllerīehrooz Mashadi I Mehdi Mahmoudi-Kaleybar II Pouyan Ahmadizadeh I Atta Oveisi III Proposed integrated driver/DYC controller is examined on lane change manuvers andthe sensitivity of the control system is investigated through the changes in the driver model and vehicle parameters. Genetic Algorithm as an intelligent optimization method is utilized to adapt PID controller gains for various working situations. A PID controller with optimized gains is used for the control of integrated driver/DYC system. Then, the controller determines and applies a corrective steering angle and a direct yaw moment to make the vehicle follow the desired path.
Thus, the driver previewed distance, the heading error and the lateral deviation between the vehicle and desired path are used as inputs. In this paper, an integrated driver/DYC control system is presented that regulates the steering angle and yaw moment, considering driver previewed path. Vehicle path following control with the presence of driver commands can be regarded as one of the important issues in vehicle active safety systems development and more realistic explanation of vehicle path tracking problem. Reduction in traffic congestion and overall number of accidents, especially within the last decade, can be attributed to the enormous progress in active safety.
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