Ali Abdulhakeem Muhammed, Sha'aban Yusuf Abubakar, Salawudeen Ahmed Tijani, Haruna Zaharuddeen, Muhammad Bilyamin, Mu'azu Muhammed Bashir, Alharthi Abdullah
Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.
Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.
PLoS One. 2025 Jul 2;20(7):e0324720. doi: 10.1371/journal.pone.0324720. eCollection 2025.
The multi-axle crane, a long vehicle with high inertia, has historically struggled with steering efficiency and path-tracking performance. Various control strategies, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC), have been employed to address these challenges. However, while improving steering efficiency, these strategies have often led to poor path-tracking performance. This work presents a significant advancement in the form of an optimized MPC for improved steering control of the multi-axle crane. A bicycle model of the multi-axle crane was adopted for the work. MPC was designed, and the smell agent optimization technique (SAO) was employed to optimize the steering input weighting factor, which determines the path-tracking performance. This provided an improved and accurate path-tracking performance for different driving speed conditions. Simulation and performance evaluation of the optimized MPC for the steering system were carried out on a curved road path for three different driving speed scenarios (25, 45, and 65 km/h). The results were compared with existing steering systems that utilized the MPC using steering efficiency, dynamic stability, and path-tracking performance. Results obtained showed improvements of 13.88%, 46.02%, and 18.35% in steering efficiency for the three scenarios over the benchmark scheme. Similarly, improvements of 2.29%, 1.03%, and 4.17%, respectively, were achieved in terms of dynamic stability for the three scenarios. For lateral error, improvements of 26.78%, 26.35%, and 27.52% were achieved, while 27.44%, 29.25%, and 28.93% were achieved for the yaw angle error in the three scenarios, respectively. A 3D simulation model for the multi-axle crane was developed in AnyLogic for visual interpretation and validation of the tracking results. These results showed that the developed MPC steering system achieved better steering performance than the existing scheme.
多轴起重机是一种具有高惯性的长型车辆，一直以来在转向效率和路径跟踪性能方面存在困难。包括比例积分微分（PID）、线性二次调节器（LQR）和模型预测控制（MPC）在内的各种控制策略已被用于应对这些挑战。然而，在提高转向效率的同时，这些策略往往导致路径跟踪性能不佳。这项工作提出了一种优化的MPC，以显著改进多轴起重机的转向控制。该工作采用了多轴起重机的自行车模型。设计了MPC，并采用嗅觉剂优化技术（SAO）来优化转向输入加权因子，该因子决定了路径跟踪性能。这为不同行驶速度条件提供了改进且准确的路径跟踪性能。针对三种不同行驶速度场景（25、45和65公里/小时），在弯曲道路上对优化后的MPC转向系统进行了仿真和性能评估。将结果与使用MPC的现有转向系统在转向效率、动态稳定性和路径跟踪性能方面进行了比较。结果表明，与基准方案相比，三种场景下的转向效率分别提高了13.88%、46.02%和18.35%。同样，三种场景下的动态稳定性分别提高了2.29%、1.03%和4.17%。对于横向误差，分别提高了26.78%、26.35%和27.52%，而三种场景下的偏航角误差分别提高了27.44%、29.25%和28.93%。在AnyLogic中开发了多轴起重机的三维仿真模型，用于对跟踪结果进行可视化解释和验证。这些结果表明，所开发的MPC转向系统比现有方案具有更好的转向性能。
