Vehicle Yaw Control Using an Active Front Steering System with Measurements of Lateral Tire Forces

2010 ◽  
Vol 2010.5 (0) ◽  
pp. 319-324
Author(s):  
Nobutaka Wada ◽  
Akihiro Takahashi ◽  
Masami Saeki ◽  
Masaharu Nishimura
Keyword(s):  
Steering System ◽  
Yaw Control ◽  
Active Front Steering ◽  
Tire Forces

Vehicle Yaw Control Using an Active Front Steering System with Measurements of Lateral Tire Forces

2011 ◽  
Vol 23 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Nobutaka Wada ◽  
◽  
Akihiro Takahashi ◽  
Masami Saeki ◽  
Masaharu Nishimura ◽  
...  
Keyword(s):  
Closed Loop ◽  
Design Method ◽  
Steering System ◽  
Front Wheel ◽  
Slip Angle ◽  
Yaw Control ◽  
Tire Force ◽  
Active Front Steering ◽  
Simulation Results ◽  
Tire Forces

We have proposed a design method of an active front wheel steering controller that guarantees closed-loop stability under lateral tire force saturation. The controller uses lateral tire force information to counteract destabilization caused by such saturation. The controller suppresses slip angle magnitude while lateral tire force is saturated. Numerical simulation results confirmed the effectiveness of the proposed method.

Research on the Control Strategy of Active Front Steering System

2013 ◽  
Vol 13 (21) ◽  
pp. 4463-4469
Author(s):  
Shuo Zhang ◽  
Qiang Yu ◽  
Chenyu Zhou ◽  
Peilong Shi ◽  
Peipei Zhang
Keyword(s):  
Control Strategy ◽  
Steering System ◽  
Active Front Steering

Research on Yaw Stability Control of Active Front Steering Based on BP Neural Network PID

2013 ◽  
Vol 765-767 ◽  
pp. 1903-1907
Author(s):  
Jie Wei ◽  
Guo Biao Shi ◽  
Yi Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Steering System ◽  
Stability Control ◽  
Steering Angle ◽  
System A ◽  
Active Front Steering ◽  
Yaw Stability ◽  
Neural Network Pid

This paper proposes using BP neural network PID to improve the yaw stability of the vehicle with active front steering system. A dynamic model of vehicle with active front steering is built firstly, and then the BP neural network PID controller is designed in detail. The controller generates the suitable steering angle so that the vehicle follows the target value of the yaw rate. The simulation at different conditions is carried out based on the fore established model. The simulation results show the BP neural network PID controller can improve the vehicles yaw stability effectively.

Optimal Composite Nonlinear Feedback Controller for an Active Front Steering System

2014 ◽  
Vol 554 ◽  
pp. 526-530 ◽  
Author(s):  
Liyana Ramli ◽  
Yahya M. Sam ◽  
Zaharuddin Mohamed ◽  
Muhamad Khairi Aripin ◽  
Muhamad Fahezal Ismail
Keyword(s):  
Parameter Optimization ◽  
Optimization Problems ◽  
Steering System ◽  
Stability Control ◽  
Feedback Controller ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Fast Tracking ◽  
Active Front Steering ◽  
Yaw Stability

Yaw stability control is the most popular topics in the automotive field. Several studies have been done in searching the effective method in controlling yaw moment. Hence, an integration of the active front steering system (AFS) with Composite Nonlinear Feedback controller is presented in this paper. Recently, this controller has been used by a lot of researchers in controlling their system performance due to its main advantage that can be seen in transient response which demonstrate super fast tracking. An optimal CNF feedback control problem is formulated as a parameter optimization problem with performance index and restrictions on stability. To handle such restrictions and constraint, the particle swarm optimization algorithm is applied to solve parameter optimization problems.

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