Control Strategy of Vehicle Dynamic Stability Control System on Low Tire-road Friction Condition

2008 ◽  
Vol 44 (11) ◽  
pp. 229 ◽  
Author(s):  
Liang LI
Keyword(s):  
Control System ◽  
Dynamic Stability ◽  
Control Strategy ◽  
Stability Control ◽  
Friction Condition ◽  
Vehicle Dynamic ◽  
Road Friction ◽  
Stability Control System ◽  
Dynamic Stability Control

scholarly journals MODELING AND RESEARCH OF BUS EQUIPPED WITH DYNAMIC STABILITY CONTROL SYSTEM

Mechanika ◽  
2018 ◽  
Vol 24 (1) ◽  
Author(s):  
Vytautas Dzerkelis ◽  
Vaidas Lukoševičius ◽  
Evaldas Girkontas ◽  
Robertas Keršys ◽  
Hubert Sar ◽  
...  
Keyword(s):  
Control System ◽  
Dynamic Stability ◽  
Stability Control ◽  
Stability Control System ◽  
Dynamic Stability Control

scholarly journals Dynamic Stability Control system: the CRONE approach

2017 ◽  
Vol 50 (1) ◽  
pp. 13822-13827 ◽  
Author(s):  
Nolwenn Monot ◽  
Xavier Moreau ◽  
André Benine-Neto ◽  
Audrey Rizzo ◽  
François Aioun
Keyword(s):  
Control System ◽  
Dynamic Stability ◽  
Stability Control ◽  
Stability Control System ◽  
Dynamic Stability Control

scholarly journals Vehicle Stability Control Strategy Based on Recognition of Driver Turning Intention for Dual-Motor Drive Electric Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shu Wang ◽  
Xuan Zhao ◽  
Qiang Yu
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Stability Control ◽  
Motor Drive ◽  
Vehicle Stability ◽  
Lane Change ◽  
Vehicle Stability Control ◽  
Stability Control System ◽  
The Stability

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.

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