An Adaptive Model Predictive Control Strategy for Path Following of Autonomous Vehicles Based on Tire Cornering Stiffness Estimation

Path following performance is a crucial issue for autonomous vehicles. Due to the influence of parameter uncertainties, inevitable deviation occurs during path following process. To solve this problem, an adaptive model predictive control strategy based on tire cornering stiffness estimation is proposed for path following. Firstly, the recursive-least-square (RLS) method is applied to estimate the uncertainty of tire cornering stiffness in real time. Secondly, based on the real-time update system model, a model predictive control (MPC) scheme is proposed to achieve path following. In this way, the proposed strategy can adapt to the changes of driving conditions. Finally, lane change maneuver is performed in simulation to verify the effectiveness of the proposed strategy. The results show that the lateral offset using the proposed strategy is reduced by 10% compared with the traditional MPC.