Related Experiment Video
Updated: Jun 7, 2025
10:09
Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
6.6K
PSO-NMPC control strategy based path tracking control of mining LHD (scraper)
Ya Liu1, Ping-An Peng2,3, Li-Guan Wang1
1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
Scientific Reports
|November 18, 2024
Summary
This study introduces a Particle Swarm Optimization-integrated Nonlinear Model Predictive Control (PSO-NMPC) strategy to improve the path tracking of underground Load-Haul-Dump (LHD) vehicles. The enhanced controller significantly reduces trajectory deviations, especially on complex curved paths.
Area of Science:
- Robotics and Automation
- Mining Engineering
- Control Systems
Background:
- Automation of underground articulated vehicles, specifically Load-Haul-Dump (LHD) machines, is crucial for digital and smart mining.
- Existing Nonlinear Model Predictive Control (NMPC) methods struggle with path tracking accuracy, exhibiting delays on high-curvature paths and correction lags.
Purpose of the Study:
- To develop and validate an improved control strategy for LHD path tracking.
- To address limitations in current NMPC controllers, namely turn delays and correction lags, in underground mining environments.
Main Methods:
- A Particle Swarm Optimization-NMPC (PSO-NMPC) control strategy was proposed, integrating PSO with NMPC.
- Simulations were conducted using a local tunnel path, comparing the PSO-NMPC controller against a standard NMPC controller.
Main Results:
- The PSO-NMPC strategy demonstrated significant improvements in trajectory tracking performance for LHDs.
- Maximum absolute lateral deviations were reduced by up to 89.7% on tested paths.
- The enhanced controller showed superior performance on large-curvature paths compared to straight or small-curvature paths.
Conclusions:
- The proposed PSO-NMPC control strategy effectively enhances LHD path tracking accuracy in underground mining.
- This method successfully mitigates issues of turn delay and backward lag, offering practical advancements for autonomous mining operations.
