Development of Decentralized Integrated Chassis Control for Vehicle Stability in Limit Handling

2016 ◽  
Vol 1 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Sameer Kolte ◽  
Ananth Kumar Srinivasan ◽  
Akilla Srikrishna
Keyword(s):  
Vehicle Stability ◽  
Integrated Chassis Control ◽  
Chassis Control

Integrated chassis control system to enhance vehicle stability

2000 ◽  
Vol 23 (1/2) ◽  
pp. 124 ◽  
Author(s):  
Youssef A. Ghoneim ◽  
William C. Lin ◽  
David M. Sidlosky ◽  
Hsien H. Chen ◽  
Yuen-Kwok Chin ◽  
...  
Keyword(s):  
Control System ◽  
Vehicle Stability ◽  
Integrated Chassis Control ◽  
Chassis Control System ◽  
Chassis Control

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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