Integrated optimal dynamics control of 4WD4WS electric ground vehicle with tire-road frictional coefficient estimation

Abstract This paper presents an integrated optimal dynamics control of four-wheel driving and four-wheel steering (4WD4WS) electric ground vehicles via hierarchical control methodology. In the higher-level design, an LQR controller is proposed to obtain the integrated lateral force and yaw moment, according to their respective reference values. The lower-level controller is designed to ensure all the tires work in the stable region while realizing the tracking control of the vehicle dynamics. The tire-road friction coefficient is estimated through the integrated longitudinal force and lateral force, respectively, using a brush tire model. To reduce the estimation error, a novel data fusion function is employed to generate the final estimation value. Finally, the effectiveness of the proposed control and estimation strategies is validated via CarSim–Simulink joint simulation.

[1]  Rajesh Rajamani,et al.  GPS-based real-time identification of tire-road friction coefficient , 2002, IEEE Trans. Control. Syst. Technol..

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

[3]  Changsun Ahn,et al.  Robust estimation of road friction coefficient , 2011, Proceedings of the 2011 American Control Conference.

[4]  Eiichi Ono,et al.  Vehicle dynamics integrated control for four-wheel-distributed steering and four-wheel-distributed traction/braking systems , 2006 .

[5]  Ossama Mokhiamar,et al.  Simultaneous Optimal Distribution of Lateral and Longitudinal Tire Forces for the Model Following Control , 2004 .

[6]  Hui Zhang,et al.  Robust Static Output Feedback Control and Remote PID Design for Networked Motor Systems , 2011, IEEE Transactions on Industrial Electronics.

[7]  Rongrong Wang,et al.  Motion Control of Four-Wheel Independently Actuated Electric Ground Vehicles considering Tire Force Saturations , 2013 .

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

[9]  Rongrong Wang,et al.  Linear Parameter-Varying Controller Design for Four-Wheel Independently Actuated Electric Ground Vehicles With Active Steering Systems , 2014, IEEE Transactions on Control Systems Technology.

[10]  Yoichi Hori,et al.  Four-wheel driving-force distribution method for instantaneous or split slippery roads for electric vehicle with in-wheel motors , 2012, 2012 12th IEEE International Workshop on Advanced Motion Control (AMC).

[11]  C. C. Chan,et al.  The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles , 2007, Proceedings of the IEEE.

[12]  Ali Ghaffari,et al.  An intelligent approach to the lateral forces usage in controlling the vehicle yaw rate , 2011 .

[13]  Junmin Wang,et al.  Observer-based tracking controller design for networked predictive control systems with uncertain Markov delays , 2012, 2012 American Control Conference (ACC).

[14]  Ali Khaki-Sedigh,et al.  Adaptive Vehicle Lateral-Plane Motion Control Using Optimal Tire Friction Forces With Saturation Limits Consideration , 2009, IEEE Transactions on Vehicular Technology.

[15]  Shou-Tao Peng On One Approach to Constraining the Combined Wheel Slip in the Autonomous Control of a 4WS4WD Vehicle , 2007, IEEE Transactions on Control Systems Technology.

[16]  Nong Zhang,et al.  Stabilizing Vehicle Lateral Dynamics With Considerations of Parameter Uncertainties and Control Saturation Through Robust Yaw Control , 2010, IEEE Transactions on Vehicular Technology.

[17]  Bin Jiang,et al.  Optimal Fault-Tolerant Path-Tracking Control for 4WS4WD Electric Vehicles , 2010, IEEE Transactions on Intelligent Transportation Systems.

[18]  Hui Zhang,et al.  Robust gain-scheduling energy-to-peak control of vehicle lateral dynamics stabilisation , 2014 .

[19]  Richard T. Meyer,et al.  Hybrid Model Predictive Power Management of A Fuel Cell‐Battery Vehicle , 2013 .