Implementation Of Robust Composite Nonlinear Feedback For Active Front Steering Based Vehicle Yaw Stability
n this paper, Robust Composite Nonlinear Feedback (CNF) was implemented on Active Front Steering (AFS) vehicle system for yaw stability control. In this control system, the main objective is to get excellent transient response of vehicle yaw rate and at the same time resist to side wind disturbance....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Penerbit UTM Press
2017
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/19206/2/2017%20Jurnal%20Teknologi%20-%20Robust%20CNF.pdf http://eprints.utem.edu.my/id/eprint/19206/ http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/11281 http://dx.doi.org/10.11113/jt.v79.11281 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | n this paper, Robust Composite Nonlinear Feedback (CNF) was implemented on Active Front Steering (AFS) vehicle system for yaw stability control. In this control system, the main objective is to get excellent transient response of vehicle yaw rate and at the same time resist to side wind disturbance. To cater unknown constant disturbance, non-integral function for Robust CNF version is used. Meanwhile for vehicle model, 7 degree of freedom vehicle body with Pacejka Tire formula model for typical passenger car is used to simulate controlled vehicle. The computer simulation by Matlab software is performed to evaluate the system performance in J-Turn and Single sine steer with magnitude from 1 to 3.1 degree with additional 400 Nm external side wind disturbance. By using typical Proportional Integration and Derivative (PID) control auto-tuned by Matlab as comparison, the new designed controller demonstrates higher capability to track reference signal faster and having minimal tracking error during disturbance occur where having less than 0.01 degree compared 0.22 degree by PID. The Robust CNF based designed control system is able to compensate disturbance effect efficiently and also has super-fast tracking as classical CNF. |
|---|
