scholarly journals Integrated chassis control for vehicle rollover prevention with neural network time-to-rollover warning metrics

2016 ◽  
Vol 8 (2) ◽  
pp. 168781401663267 ◽  
Author(s):  
Bing Zhu ◽  
Qi Piao ◽  
Jian Zhao ◽  
Litong Guo
Keyword(s):  
Neural Network ◽  
Rollover Prevention ◽  
Integrated Chassis Control ◽  
Network Time ◽  
Vehicle Rollover ◽  
Chassis Control ◽  
Rollover Warning

MPC Based Integrated Chassis Control to Enhance Vehicle Handling Considering Roll Stability

2011 ◽  
Author(s):  
Ganesh Adireddy ◽  
Taehyun Shim
Keyword(s):  
Control System ◽  
High Speed ◽  
Control Method ◽  
Torque Distribution ◽  
Vehicle Handling ◽  
Integrated Chassis Control ◽  
Wheel Torque ◽  
Vehicle Rollover ◽  
Chassis Control ◽  
Vehicle Chassis

An integrated vehicle chassis control system was developed to improve vehicle handling (yaw) responses while maintain vehicle roll stability using an 8 DOF vehicle model, a simplified tire model, and a model predictive control method. The proposed control system incorporates active wheel torque distribution, active front steering, and active anti-rollbar to enhance vehicle handling and its ability to track the desired trajectory when the risk of vehicle rollover is low. As vehicle rollover risks increase, the proposed control system shifts its control focus from only handling enhancement to vehicle roll stabilization by adjusting the gains in the controller. The simulation results show that the proposed control system can improve vehicle handling responses while ensuring vehicle roll stability at high speed vehicle maneuvers.

Unified Chassis Control for Vehicle Rollover Prevention

2008 ◽  
Vol 41 (2) ◽  
pp. 5682-5687 ◽  
Author(s):  
Jangyeol Yoon ◽  
Wanki Cho ◽  
Kyongsu Yi ◽  
Bongyeong Koo
Keyword(s):  
Rollover Prevention ◽  
Vehicle Rollover ◽  
Chassis Control

A Vehicle Rollover Warning Approach Based on Neural Network and Support Vector Machine

2013 ◽  
Vol 367 ◽  
pp. 433-440 ◽  
Author(s):  
Xiao Ping Du ◽  
Lian Tao Lu ◽  
Hua Mei Sun ◽  
Yun Li ◽  
Jiao Jiao Song
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Maximum Error ◽  
Simulation Software ◽  
Support Vector ◽  
Vehicle Rollover ◽  
Vehicle Dynamic Simulation ◽  
Warning Model ◽  
Validation Experiments ◽  
Rollover Warning

This paper studies the vehicle rollover warning based on the vehicle running state parameters which are easy for collecting. By taking advantages of neural network and support vector machine, this paper analyzes the probabilities of the vehicle rollover in the scenario of single lane change and establishes a warning model which could predict the rollover 0.4 seconds in advance for a particular type of vehicle. According to the validation experiments on Ve-DYNA which is a vehicle dynamic simulation software, the results validate the warning models good performance with the maximum error as 0.04 second and no missing cases which indicates its validity for rollover warning.

scholarly journals Integrated chassis control: Classification, analysis and future trends

2021 ◽  
Author(s):  
Victor Mazzilli ◽  
Stefano De Pinto ◽  
Leonardo Pascali ◽  
Michele Contrino ◽  
Francesco Bottiglione ◽  
...  
Keyword(s):  
Future Trends ◽  
Classification Analysis ◽  
Integrated Chassis Control ◽  
Chassis Control

scholarly journals Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bing Zhu ◽  
Yizhou Chen ◽  
Jian Zhao ◽  
Yunfu Su
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Driver Behavior ◽  
Stability Performance ◽  
Integrated Chassis Control ◽  
Yaw Moment Control ◽  
Chassis Control ◽  
Lane Change Test ◽  
Behavior Characteristics ◽  
Vehicle Chassis

An integrated vehicle chassis control strategy with driver behavior identification is introduced in this paper. In order to identify the different types of driver behavior characteristics, a driver behavior signals acquisition system was established using the dSPACE real-time simulation platform, and the driver inputs of 30 test drivers were collected under the double lane change test condition. Then, driver behavior characteristics were analyzed and identified based on the preview optimal curvature model through genetic algorithm and neural network method. Using it as a base, an integrated chassis control strategy with active front steering (AFS) and direct yaw moment control (DYC) considering driver characteristics was established by model predictive control (MPC) method. Finally, simulations were carried out to verify the control strategy by CarSim and MATLAB/Simulink. The results show that the proposed method enables the control system to adjust its parameters according to the driver behavior identification results and the vehicle handling and stability performance are significantly improved.

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