Real-time Model Predictive Controller for Vehicle Lateral Stabilization under Extreme Conditions

Under extreme driving conditions, the tire lateral force is easily saturated, which should be considered for better performance in vehicle stability control, as well as the safety constraints and real-time response. To address the above problem, a real-time model predictive controller for four wheel independent motor-drive electric vehicles is proposed to improve the lateral stability. First, considering the saturation characteristic of the tire dynamics on a slippery road, the tire model is developed into the piecewise form of linear and saturation regions, which extracts the main nonlinearity of tire. Second, the additional yaw moment is determined to achieve the control objectives of lateral stability and handling performance. Then, the additional yaw moment is distributed into torques acting on each motor by optimizing the tire load rates. Finally, co-simulations with MATLAB/CarSim and hardware-in-the-loop simulation are performed, and the fast solution of optimization problem is realized based on C-language. The results show that lateral stability and handling performance are efficiently improved, and the real-time performance can be ensured with a sampling time as 5ms.