Vehicle stability control using direct virtual sensors

The paper investigates the use of a direct virtual sensor (DVS) to replace a physical sensor in a vehicle stability control system. A yaw control system is considered and the proposed solution can be particularly useful when a fault of the yaw rate physical sensor occurs. A DVS is a stable linear filter derived directly from input–output data, collected in a preliminary experiment. In this work, it is shown that, by using data collected in a closed-loop fashion, better DVS accuracy can be obtained with a reduced number of measured variables. Moreover, the robust stability of the closed-loop system employing a DVS is studied. The effectiveness of the presented results is shown through numerical simulations of harsh manoeuvres, performed using a detailed model of a vehicle equipped with an active front steering device.

[1]  Philip Koehn,et al.  Active Steering - The BMW Approach Towards Modern Steering Technology , 2004 .

[2]  Lorenzo Fagiano,et al.  Stability control of 4WS vehicles using robust IMC techniques , 2008 .

[3]  Giancarlo Genta,et al.  Motor Vehicle Dynamics , 1997 .

[4]  Anton van Zanten,et al.  VDC, The Vehicle Dynamics Control System of Bosch , 1995 .

[5]  K. Poolla,et al.  Filter design from data: direct vs. two-step approaches , 2006, 2006 American Control Conference.

[6]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[7]  Lorenzo Fagiano,et al.  Comparing rear wheel steering and rear active differential approaches to vehicle yaw control , 2010 .

[8]  Aleksander B. Hac,et al.  Estimation of Vehicle Side Slip Angle and Yaw Rate , 2000 .

[9]  S. Diop,et al.  An observer and an integrated braking/traction and steering control for a cornering vehicle , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Graham C. Goodwin,et al.  Virtual sensors for control applications , 2002, Annu. Rev. Control..

[11]  Garrick J. Forkenbrock,et al.  NHTSA's Light Vehicle Handling and ESC Effectiveness Research Program , 2005 .

[12]  M. Canale,et al.  A robust IMC approach for stability control of 4WS vehicles , 2007, 2007 American Control Conference.

[13]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[14]  Jürgen Ackermann,et al.  Robust yaw damping of cars with front and rear wheel steering , 1993, IEEE Trans. Control. Syst. Technol..

[15]  Mario Milanese,et al.  Direct data-driven filter design for uncertain LTI systems with bounded noise , 2010, Autom..

[16]  Wonshik Chee.  Yaw rate estimation using two 1-axis accelerometers , 2005, Proceedings of the 2005, American Control Conference, 2005..

[17]  Gao Zhenhai.  Soft sensor application in vehicle yaw rate measurement based on Kalman filter and vehicle dynamics , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[18]  Lorenzo Fagiano,et al.  A study on the use of virtual sensors in vehicle control , 2008, 2008 47th IEEE Conference on Decision and Control.

[19]  Bilin Aksun Güvenç,et al.  Robust two degree-of-freedom vehicle steering controller design , 2004, IEEE Transactions on Control Systems Technology.

[20]  Mario Milanese,et al.  Virtual sensors for linear dynamic systems: structure and identification , 2007 .

[21]  Laura Ryan Ray,et al.  Nonlinear state and tire force estimation for advanced vehicle control , 1995, IEEE Trans. Control. Syst. Technol..

[22]  Lorenzo Fagiano,et al.  Robust vehicle yaw control using an active differential and IMC techniques , 2007 .

[23]  A. T. van Zanten,et al.  Bosch ESP Systems: 5 Years of Experience , 2000 .

[24]  Gene F. Franklin,et al.  An error bound for a discrete reduced order model of a linear multivariable system , 1987 .