Intelligent Vehicle Trajectory Tracking Control Using ILQR-MPC

Trajectory tracking control is a core technology in intelligent vehicle autonomous driving systems, directly influencing both driving safety and control accuracy. To overcome the limitations of traditional model predictive control (MPC) in real-time performance under complex operating conditions, as well as the limited robustness of linear quadratic regulators (LQR) against system uncertainties, this article proposes a hybrid iterative LQR–MPC (ILQR-MPC) control strategy. First, a dynamic model of the intelligent vehicle is developed to capture its behavior during high-speed driving and cornering. Next, an ILQR-MPC hybrid framework is designed. By exploiting the rapid iterative optimization capabilities of the ILQR algorithm, an initial control sequence is generated for the MPC, thereby reducing the computational load during MPC’s online rolling-horizon optimization. This approach preserves MPC’s advantages in handling constraints and maintaining robustness against parameter variations and external disturbances. Finally, joint simulations using MATLAB/Simulink and CarSim are conducted to evaluate the proposed approach against conventional MPC under standard road conditions, curved sections, and sudden changes in road friction. The results show that the ILQR-MPC strategy reduces trajectory tracking errors, shortens computational time, and maintains excellent stability and robustness under complex operating conditions.