Real-Time High-Precision Nonlinear Tracking Control of Autonomous Vehicles Using Fast Iterative Model Predictive Control

Model predictive control (MPC) has become a practical approach for implementing active safety control strategies in autonomous vehicles (AVs). This paper introduces a Fast Iterative MPC (FI-MPC) framework with rule-based iteration convergence criteria to tackle the significant challenges of control precision and solving time discrepancies between linear and nonlinear MPC algorithms within tracking control strategies for AVs. Differing from linear and nonlinear MPC structures, the core concept of FI-MPC involves incorporating the unmodeled nonlinear dynamics of the vehicle or tires into a linear framework to achieve optimal iterative control solutions. The goal is to mitigate the mismatch between linear and high-fidelity nonlinear dynamics models, thus achieving real-time high-precision tracking control of nonlinear vehicle dynamics under challenging conditions. In addition, rule-based convergence criteria are designed to enhance the iteration mechanism of FI-MPC, further conserving the computational resources of the onboard controller by reducing unnecessary iteration steps. To validate the effectiveness of the FI-MPC algorithm, this study investigates the trajectory tracking problem for a four-wheel-drive AV for a weave test scenario. Co-simulation and hardware-in-the-loop testing results at various speeds confirm that FI-MPC enables high-precision control of nonlinear dynamics while ensuring excellent real-time performance.