Scholarlink

<p>Path tracking of articulated wheel loaders (AWLs) is challenging because the articulation geometry, hydraulic steering limits, and vehicle speed are strongly coupled, especially in confined loading yards and quarry environments. Sharp turns may push the machine close to its articulation and lateral-acceleration limits, making both tracking accuracy and motion safety important. This study presents a comparative control framework for evaluating the effects of model fidelity, actuator constraints, and longitudinal–lateral coupling on AWL trajectory tracking. A joint-centered nonlinear kinematic model is used to relate articulation angle to path curvature. The plant model includes first-order hydraulic steering dynamics, articulation-magnitude limits, and articulation-rate limits. Based on this model, two optimized geometric controllers, modified Pure Pursuit (PP-Mod) and modified Stanley (ST-Mod), are compared with three predictive controllers: Linear Model Predictive Control (LMPC), Nonlinear Model Predictive Control (NMPC), and the proposed Enhanced Model Predictive Control (EMPC). Unlike fixed-speed predictive controllers, EMPC includes longitudinal acceleration as a decision variable, allowing online speed adaptation according to the upcoming curvature demand. The controllers are tested on a 673.30 m closed-loop AWL trajectory under nominal and aggressive operating conditions. The optimized geometric controllers provide accurate tracking with negligible computation time. Among the predictive controllers, NMPC improves tracking compared with LMPC, but its fixed-speed structure leads to high lateral-acceleration demand in sharp turns. Under aggressive operation, EMPC reduces the RMS cross-track error from 0.3030 to 0.2553 m compared with NMPC and decreases the maximum lateral acceleration from 0.5349 to 0.3281g, corresponding to a 38.7% reduction. Sensitivity and combined robustness tests further show that EMPC reduces large transient deviations and avoids sustained lateral-acceleration threshold violations under actuator-delay mismatch, effective-geometry perturbation, and measurement noise. These results indicate that explicit speed adaptation within predictive optimization provides a balanced trade-off between tracking accuracy, cycle time, and lateral-acceleration regulation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Coupled Longitudinal–Lateral Predictive Control for Path Tracking of Articulated Wheel Loaders

  • 期刊论文

  • 发表日期：

    2026年7月27日

查看全文
  • Umit Onen,
  • Abdulsamed Tabak,
  • Baris Gokce

摘要

Path tracking of articulated wheel loaders (AWLs) is challenging because the articulation geometry, hydraulic steering limits, and vehicle speed are strongly coupled, especially in confined loading yards and quarry environments. Sharp turns may push the machine close to its articulation and lateral-acceleration limits, making both tracking accuracy and motion safety important. This study presents a comparative control framework for evaluating the effects of model fidelity, actuator constraints, and longitudinal–lateral coupling on AWL trajectory tracking. A joint-centered nonlinear kinematic model is used to relate articulation angle to path curvature. The plant model includes first-order hydraulic steering dynamics, articulation-magnitude limits, and articulation-rate limits. Based on this model, two optimized geometric controllers, modified Pure Pursuit (PP-Mod) and modified Stanley (ST-Mod), are compared with three predictive controllers: Linear Model Predictive Control (LMPC), Nonlinear Model Predictive Control (NMPC), and the proposed Enhanced Model Predictive Control (EMPC). Unlike fixed-speed predictive controllers, EMPC includes longitudinal acceleration as a decision variable, allowing online speed adaptation according to the upcoming curvature demand. The controllers are tested on a 673.30 m closed-loop AWL trajectory under nominal and aggressive operating conditions. The optimized geometric controllers provide accurate tracking with negligible computation time. Among the predictive controllers, NMPC improves tracking compared with LMPC, but its fixed-speed structure leads to high lateral-acceleration demand in sharp turns. Under aggressive operation, EMPC reduces the RMS cross-track error from 0.3030 to 0.2553 m compared with NMPC and decreases the maximum lateral acceleration from 0.5349 to 0.3281g, corresponding to a 38.7% reduction. Sensitivity and combined robustness tests further show that EMPC reduces large transient deviations and avoids sustained lateral-acceleration threshold violations under actuator-delay mismatch, effective-geometry perturbation, and measurement noise. These results indicate that explicit speed adaptation within predictive optimization provides a balanced trade-off between tracking accuracy, cycle time, and lateral-acceleration regulation.