An Enhancement to an Electronic Stability Control System to Include a Rollover Control Function

2007 ◽  
Author(s):  
Jianbo Lu ◽  
Dave Messih ◽  
Albert Salib ◽  
Dave Harmison
Keyword(s):  
Control System ◽  
Control Function ◽  
Stability Control ◽  
Electronic Stability Control ◽  
Stability Control System

Three Axes MEMS Combined Sensor for Electronic Stability Control System

2011 ◽  
Vol 131 (8) ◽  
pp. 279-285
Author(s):  
Heewon Jeong ◽  
Yasushi Goto ◽  
Takanori Aono ◽  
Toshiaki Nakamura ◽  
Masahide Hayashi
Keyword(s):  
Control System ◽  
Stability Control ◽  
Electronic Stability Control ◽  
Stability Control System ◽  
Combined Sensor

Three-axis MEMS combined sensor for electronic stability control system

2012 ◽  
Vol 95 (7) ◽  
pp. 49-57 ◽  
Author(s):  
Heewon Jeong ◽  
Yasushi Goto ◽  
Takanori Aono ◽  
Toshiaki Nakamura ◽  
Masahide Hayashi
Keyword(s):  
Control System ◽  
Stability Control ◽  
Electronic Stability Control ◽  
Stability Control System ◽  
Combined Sensor

scholarly journals Development and Validation of Electronic Stability Control System Algorithm Based on Tire Force Observation

2020 ◽  
Vol 10 (23) ◽  
pp. 8741
Author(s):  
Dang Lu ◽  
Yao Ma ◽  
Hengfeng Yin ◽  
Zhihui Deng ◽  
Jiande Qi
Keyword(s):  
Control System ◽  
Lower Layer ◽  
Linear Quadratic Regulator ◽  
Stability Control ◽  
Electronic Stability Control ◽  
Linear Quadratic ◽  
Tire Force ◽  
Stability Control System ◽  
Feedback Correction ◽  
Development And Validation

In view of the higher and higher assembly rate of the electronic stability control system (ESC in short), the control accuracy still needs to be improved. In order to make up for the insufficient accuracy of the tire model in the nonlinear area of the tire, in this paper, an algorithm for the electronic stability control system based on the control of tire force feedforward used in conjunction with tire force sensors is proposed. The algorithm takes into consideration the lateral stability of the tire under extreme conditions affected by the braking force. We use linear optimal control to determine the optimal yaw moment, and obtain the brake wheel cylinder pressure through an algorithm combining feedforward compensation based on measured tire force and feedback correction. The controller structure is divided into two layers, the upper layer is controlled by a linear quadratic regulator (LQR in short) and the lower layer is controlled by PID (Proportional-integral-derivative) and feedforward. After that, verification of the controller’s algorithms using software cosimulation and hardware-in-the-loop (HIL in short) testing in the double lane change (DLC in short) and sine with dwell (SWD in short) conditions. From the test results it can be concluded that the controller based on tire force observation has partially control advantages.

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