Adaptive LTV-MPC-Based Path Tracking and Steering Coordination for Four-Wheel Steering Vehicles in Parallel Parking

To address the limited maneuverability and tracking accuracy of autonomous four-wheel steering (4WS) vehicles during parallel parking in confined spaces, an adaptive linear time-varying model predictive control (LTV-MPC) strategy for integrated path tracking and steering coordination is proposed. Different from conventional MPC-based parking controllers with fixed weighting parameters and steering allocation schemes, the proposed method introduces an adaptive weighting mechanism that adjusts the tracking-error weights online according to the yaw-angle error, thereby improving the balance between tracking accuracy and control smoothness throughout the parking process. A hyperbolic tangent (tanh)-based steering allocation strategy is further developed to realize smooth transitions between reverse-phase steering and posture adjustment, while a PID compensation module is incorporated to improve steering command execution. A kinematic single-track model considering Ackermann steering geometry is established to formulate the prediction model of the controller. MATLAB/Simulink and CarSim co-simulation, together with hardware-in-the-loop (HIL) experiments, are conducted to evaluate the proposed control strategy. The experimental results demonstrate that the proposed method achieves more accurate path tracking, smoother steering responses, and higher parking stability than conventional controllers under typical parallel parking scenarios. The proposed strategy provides an effective and practical control framework for improving the low-speed maneuverability and path-tracking performance of autonomous 4WS vehicles.