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

Abdulhakeem Muhammed Ali1, Yusuf Abubakar Sha'aban2, Ahmed Tijani Salawudeen3

  • 1Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria.

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This study introduces an optimized Model Predictive Control (MPC) for multi-axle cranes, significantly enhancing steering efficiency and path-tracking performance. The new system improves stability and reduces errors across various speeds.

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Area of Science:

  • Robotics and Control Systems
  • Mechanical Engineering
  • Automotive Engineering

Background:

  • Multi-axle cranes exhibit poor steering efficiency and path-tracking due to high inertia.
  • Existing control strategies like PID, LQR, and standard MPC offer trade-offs between steering efficiency and path-tracking.
  • A need exists for advanced control systems to improve multi-axle crane maneuverability.

Purpose of the Study:

  • To develop and evaluate an optimized Model Predictive Control (MPC) for enhanced steering control in multi-axle cranes.
  • To improve both steering efficiency and path-tracking performance simultaneously.
  • To validate the proposed control strategy across different driving speeds and conditions.

Main Methods:

  • A bicycle model was adopted to represent the multi-axle crane dynamics.
  • Model Predictive Control (MPC) was designed for the steering system.
  • The Smell Agent Optimization (SAO) technique was employed to optimize the steering input weighting factor within the MPC framework.
  • Simulations were conducted on a curved road path at speeds of 25, 45, and 65 km/h.
  • A 3D simulation model was developed in AnyLogic for visual validation.

Main Results:

  • The optimized MPC demonstrated significant improvements in steering efficiency (up to 46.02%) across different speeds.
  • Dynamic stability was enhanced, with improvements ranging from 1.03% to 4.17%.
  • Path-tracking performance showed substantial gains, with lateral error reduced by up to 27.52% and yaw angle error by up to 29.25%.
  • The optimized system outperformed existing MPC steering schemes in simulations.

Conclusions:

  • The optimized MPC, utilizing SAO for tuning, provides superior steering control for multi-axle cranes.
  • The proposed method effectively balances steering efficiency and path-tracking performance.
  • The AnyLogic 3D simulation validated the enhanced maneuverability and tracking accuracy of the developed system.