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

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

SUBMITTER: Ali AM 

PROVIDER: S-EPMC12221097 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature