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.
Ji et al. (Fri,) studied this question.