Abstract:

To address the issue of steering lag and improve the accuracy of lateral control in unmanned mining trucks, this study proposed a lateral control algorithm based on fuzzy model predictive control (FMPC). First, the vehicle dynamics model and tracking error model were established. Subsequently, a vehicle state prediction method based on dynamic preview time was designed, and the tracking error was calculated according to the predicted vehicle state after the preview period. Furthermore, by integrating fuzzy control with model predictive control (MPC), an MPC controller was developed that adaptively adjusts the weight matrices of both lateral error and heading angle error. The effectiveness of the proposed FMPC algorithm was validated through hardware-in-the-loop simulation experiments and real-vehicle tests. The results indicate that, in the hardware-in-the-loop simulation, the maximum lateral error of the FMPC algorithm is reduced by 43.0% compared to the Pure Pursuit algorithm. In real-vehicle experiments conducted under two operational conditions—empty-load uphill driving and heavy-load parking—the maximum lateral errors are reduced by 50.1% and 17.6%, respectively, in comparison to the Pure Pursuit algorithm, demonstrating that the FMPC algorithm achieves superior control performance and significantly enhances the lateral control accuracy of unmanned mining trucks.

Key words: unmanned mining trucks, lateral control, steering lag, dynamic preview, fuzzy model predictive control (FMPC)