Optimized Model Predictive Control for improving dynamic stability and steering accuracy in multi-axle cranes

Abdulhakeem Muhammed Ali, Yusuf Abubakar Sha’aban, Ahmed Tijani Salawudeen, Zaharuddeen Haruna, Bilyamin Muhammad, Muhammed Bashir Mu’azu and Abdullah Alharthi

PLOS ONE, 2025, vol. 20, issue 7, 1-30

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0324720

DOI: 10.1371/journal.pone.0324720

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