To address the performance testing requirements of autonomous vehicles (AVs),
this study proposes a model predictive control (MPC) algorithm specifically
designed for low-ground-clearance test target vehicles (TTVs) to achieve
trajectory tracking control. First, the kinematic model of the TTV is
established, and its state-space equations are derived. An objective
optimization function incorporating both error weighting and control weighting
is designed. Simulation analysis reveals the influence of the control error
weighting ratio (CEWR) on both straight-line and curved trajectory tracking
performance: For straight-line tracking, increasing the CEWR from 10 to 25
reduces the overshoot, but increases the distance required to reach the target
trajectory by 4.7%. A similar pattern is observed in curved trajectory tracking.
To overcome the limitations of the fixed CEWR, an improved MPC algorithm
integrating fuzzy control is proposed. This algorithm dynamically adjusts the
CEWR in real time based on deviations from the target trajectory to achieve
adaptive optimization. Simulation results demonstrate that for straight-line
tracking, the MPC-Fuzzy eliminates overshoot entirely and reduces the
stabilization time on the target trajectory by approximately 12.68%, 14.90%, and
15.73% compared to three MPCs with fixed CEWR, respectively. For curved
trajectory tracking, the MPC-Fuzzy algorithm achieves faster trajectory
convergence while simultaneously reducing overshoot to some extent,
demonstrating effective compromise control performance.
