Adaptive Vehicle Speed Control With Input Injections for Longitudinal Motion Independent Road Frictional Condition Estimation

This paper presents a novel real-time tire-road friction coefficient estimation method that is independent of vehicle longitudinal motion for ground vehicles with separable control of the front and rear wheels. The tire-road friction coefficient information is of critical importance for vehicle dynamic control systems and intelligent autonomous vehicle applications. In this paper, the vehicle longitudinal-motion-independent tire-road friction coefficient estimation method consists of three main components: 1) an observer to estimate the internal state of a dynamic LuGre tire model; 2) an adaptive control law with a parameter projection mechanism to track the desired vehicle longitudinal motion in the presence of tire-road friction coefficient uncertainties and actively injected braking excitation signals; and 3) a recursive least square estimator that is independent of the control law, to estimate the tire-road friction coefficient in real time. Simulation results based on a high-fidelity CarSim full-vehicle model show that the system can reliably estimate the tire-road friction coefficient independent of vehicle longitudinal motion.

[1]  Kamesh Subbarao,et al.  A novel parameter projection mechanism for smooth and stable adaptive control , 2005, Syst. Control. Lett..

[2]  Fredrik Gustafsson,et al.  Slip-based tire-road friction estimation , 1997, Autom..

[3]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[4]  Roberto Horowitz,et al.  Dynamic Friction Model-Based Tire-Road Friction Estimation and Emergency Braking Control , 2005 .

[5]  Yih-Ping Luh,et al.  Design and control of axial-flux brushless DC wheel motors for electric Vehicles-part I: multiobjective optimal design and analysis , 2004 .

[6]  Fei-Yue Wang,et al.  Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control , 2006, IEEE Transactions on Intelligent Transportation Systems.

[7]  Junmin Wang,et al.  Autonomous ground vehicle control system for high-speed and safe operation , 2009 .

[8]  C. Canudas-de-Wit,et al.  Observers for tire/road contact friction using only wheel angular velocity information , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[9]  Junmin Wang,et al.  Vehicle yaw-inertia- and mass-independent adaptive steering control , 2009 .

[10]  Junmin Wang,et al.  Combined Tire Slip and Slip Angle Tracking Control for Advanced Vehicle Dynamics Control Systems , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[11]  Kyongsu Yi,et al.  Estimation of Tire-Road Friction Using Observer Based Identifiers , 1999 .

[12]  Junmin Wang,et al.  Autonomous ground vehicle control system for high-speed and safe operation , 2008, 2008 American Control Conference.

[13]  Rajesh Rajamani,et al.  GPS-Based Real-Time Identification of Tire-Road Friction Coefficient , 2002 .

[14]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[15]  Laura E. Ray,et al.  All-wheel driving using independent torque control of each wheel , 2003, Proceedings of the 2003 American Control Conference, 2003..

[16]  K. Hedrick,et al.  Real-time slip-based estimation of maximum tire-road friction coefficient , 2004, IEEE/ASME Transactions on Mechatronics.

[17]  Marian P. Kazmierkowski,et al.  Direct torque control of PWM inverter-fed AC motors - a survey , 2004, IEEE Transactions on Industrial Electronics.

[18]  Changsun Ahn,et al.  Estimation of road friction for enhanced active safety systems: Dynamic approach , 2009, 2009 American Control Conference.

[19]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[20]  Hans B. Pacejka,et al.  THE MAGIC FORMULA TYRE MODEL , 1991 .

[21]  Junmin Wang,et al.  Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors , 2011 .

[22]  Jiangping Wang,et al.  The impact of adaptive cruise control systems on highway safety and traffic flow , 2004 .

[23]  Anuradha M. Annaswamy,et al.  Stability and robustness properties of a simple adaptive controller , 1996, IEEE Trans. Autom. Control..

[24]  Junmin Wang,et al.  Friction estimation on highway vehicles using longitudinal measurements , 2004 .

[25]  P. Olver Nonlinear Systems , 2013 .

[26]  P. Tsiotras,et al.  A LuGre tire friction model with exact aggregate dynamics , 2004, Proceedings of the 2004 American Control Conference.

[27]  Changsun Ahn,et al.  Estimation of road friction for enhanced active safety systems: Algebraic approach , 2009, 2009 American Control Conference.

[28]  Sung-Ho Hwang,et al.  Clamping-Force Control for Electromechanical Brake , 2010, IEEE Transactions on Vehicular Technology.

[29]  Yih-Ping Luh,et al.  Design and control of axial-flux brushless DC wheel motors for electric Vehicles-part II: optimal current waveforms and performance test , 2004 .

[30]  Roberto Horowitz,et al.  Adaptive emergency braking control with underestimation of friction coefficient , 2002, IEEE Trans. Control. Syst. Technol..

[31]  Rajesh Rajamani,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2012 .

[32]  Rajesh Rajamani,et al.  Should adaptive cruise-control systems be designed to maintain a constant time gap between vehicles? , 2001, IEEE Transactions on Vehicular Technology.

[33]  Petros A. Ioannou,et al.  Robust Adaptive Control , 2012 .

[34]  Junmin Wang,et al.  Coordinated and Reconfigurable Vehicle Dynamics Control , 2009, IEEE Transactions on Control Systems Technology.

[35]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[36]  C. Canudas de Wit,et al.  Dynamic tire friction models for vehicle traction control , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[37]  W. Marsden I and J , 2012 .