Vehicle Sideslip Estimation Design , implementation , and experimental validation

Control systems that help the driver avoid accidents, or limit the damage in case of an accident, have become ubiquitous in modern passenger cars. For example, new cars typically have an anti-lock braking system (ABS), which prevents the wheels from locking during hard braking, and they often have an electronic stability control system (ESC), which stabilizes the lateral motion of the vehicle to prevent skidding. Collision warning and avoidance, rollover prevention, crosswind stabilization, and preparation for an impending accident by adjusting seat positions and seat belts are additional examples of control systems for automotive safety. These systems rely on information about the state of the vehicle and its surroundings. To obtain this information, modern cars are equipped with various sensors. For a typical car with an ESC system, necessary measurements include the steering wheel angle, wheel angular velocities, lateral acceleration, and the rate of rotation around the vertical body-fixed axis, known as the yaw rate. These measurements alone contain a great deal of information about the state of the vehicle. The speed of the car can be estimated using the wheel angular velocities, and a linear reference model taking the speed, steering wheel angle, and additional measurements as inputs can be used to predict the behavior of the car under normal driving conditions. The predicted behavior can be compared to the actual behavior of the car; ESC systems, for example, use the brakes to correct the deviation from a yaw reference model when the vehicle starts to skid [1]. Although some quantities are easily measured, others are difficult to measure because of high cost or impracticality. When some quantity cannot be measured directly, it is often necessary to estimate it using the measurements that are available. Observers combine the available measurements with dynamic models to estimate unknown dynamic states. Often, dynamic models of sufficient accuracy are not available, and must be carefully constructed as part of the observer design. The observer estimates can be used to implement control algorithms, as Figure 1 illustrates.

[1]  Sebastien Glaser,et al.  Road bank angle and faults estimation using unknown input proportional-integral observer , 2007, 2007 European Control Conference (ECC).

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

[3]  Laura R. Ray,et al.  Nonlinear Tire Force Estimation and Road Friction Identification: Simulation and Experiments, , 1997, Autom..

[4]  Huei Peng,et al.  A study on lateral speed estimation methods , 2004 .

[5]  H.F. Grip,et al.  Nonlinear Vehicle Velocity Observer with Road-Tire Friction Adaptation , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[6]  Hongtei Eric Tseng,et al.  Dynamic Estimation of Road Bank Angle , 2001 .

[7]  David M. Bevly,et al.  Integrating INS Sensors With GPS Measurements for Continuous Estimation of Vehicle Sideslip, Roll, and Tire Cornering Stiffness , 2006, IEEE Transactions on Intelligent Transportation Systems.

[8]  Matt C. Best,et al.  An Extended Adaptive Kalman Filter for Real-time State Estimation of Vehicle Handling Dynamics , 2000 .

[9]  Tor Arne Johansen,et al.  On non-linear unknown input observers–applied to lateral vehicle velocity estimation on banked roads , 2007, Int. J. Control.

[10]  Jon Rigelsford,et al.  Automotive Control Systems: For Engine, Driveline and Vehicle , 2004 .

[11]  Pierre Pettersson,et al.  Estimation of Vehicle Lateral Velocity , 2008 .

[12]  Tor Arne Johansen,et al.  Nonlinear vehicle side-slip estimation with friction adaptation , 2008, Autom..

[13]  Yoshiki Fukada,et al.  SLIP-ANGLE ESTIMATION FOR VEHICLE STABILITY CONTROL , 1999 .

[14]  J. Christian Gerdes,et al.  Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control , 2004 .

[15]  David M. Bevly,et al.  Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles , 2004 .

[16]  J. Christian Gerdes,et al.  The Use of GPS Based Velocity Measurements for Measurement of Sideslip and Wheel Slip , 2002 .

[17]  Uwe Kiencke,et al.  Determination of the vehicle body side slip angle with non-linear observer strategies , 2005 .

[18]  Tor Arne Johansen,et al.  Nonlinear Observer for Vehicle Velocity with Friction and Road Bank Angle Adaptation - Validation and Comparison with an Extended Kalman Filter , 2007 .

[19]  G. Bierman.  Factorization methods for discrete sequential estimation , 1977 .

[20]  Hyeongcheol Lee,et al.  Reliability Indexed Sensor Fusion and Its Application to Vehicle Velocity Estimation , 2006 .

[21]  Uwe Kiencke,et al.  NONLINEAR OBSERVER DESIGN FOR LATERAL VEHICLE DYNAMICS , 2005 .

[22]  Willy Klier,et al.  Robust Estimation of Vehicle Sideslip Angle - An Approach w/o Vehicle and Tire Models , 2008 .

[23]  Hans B. Pacejka,et al.  Tire and Vehicle Dynamics , 1982 .

[24]  Tor Arne Johansen,et al.  Vehicle velocity estimation using nonlinear observers , 2006, Autom..

[25]  Uwe Kiencke,et al.  Extended Kalman Filter for Vehicle Dynamics Determination Based on a Nonlinear Model Combining Longitudinal and Lateral Dynamics , 2007 .

[26]  Paul J.Th. Venhovens,et al.  Vehicle Dynamics Estimation Using Kalman Filters , 1999 .

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

[28]  Uwe Kiencke,et al.  Observation of Lateral Vehicle Dynamics , 1996 .

[29]  Antonio Loría,et al.  A nested Matrosov theorem and persistency of excitation for uniform convergence in stable nonautonomous systems , 2005, IEEE Transactions on Automatic Control.