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.
