  With the development of autonomous driving technology, there are increasing
demands for vehicle control, and MPC has become a widely researched topic in
both industry and academia. Existing MPC control methods based on vehicle
kinematics or dynamics have challenges such as difficult modeling, numerous
parameters, strong nonlinearity, and high computational cost. To address these
issues, this paper adapts an existing Data-driven MPC control method and
applies it to autonomous vehicle steering control. This method avoids the need
for complex vehicle system modeling and achieves trajectory tracking with
relatively low computational time and small errors. We validate the control
effectiveness of the algorithm in specific scenario through CarSim-Simulink
simulation and perform comparative analysis with PID and vehicle kinematics
MPC, confirming the feasibility and superiority of it for vehicle steering
control.
