Trajectory Tracking Control of Driverless Racing Car Under Extreme Conditions

Abstract

Aiming at the problem that it is difficult to ensure the trajectory tracking accuracy and driving stability of driverless racing car under the extreme conditions of high-speed turning with different road adhesion coefficients, a trajectory tracking control strategy is proposed. Firstly, the road adhesion coefficient is estimated using the extended Kalman filter algorithm. Draw the phase plane diagram of the vehicle’s centroid sideslip angle-centroid sideslip angular velocity. Use the two-line method to determine the phase plane stability area and obtain the expected limit vehicle speed under different road adhesion coefficients and different front wheel steering angle. Tracking of the desired limit travel speed is achieved through drive and brake control. Secondly, a predictive control algorithm based on adaptive prediction horizon model is designed as a lateral motion control strategy to improve the trajectory tracking accuracy. Finally, using MATLAB/Simulink and CarSim co-simulation, the results show that the proposed control strategy can ensure the driving stability of the driverless racing car and improve the trajectory tracking accuracy under extreme conditions.

Keywords

• Adaptive prediction horizon model predictive control
• driverless racing car
• extreme conditions
• trajectory tracking control