Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

Guoxing Bai,Yu Meng,Baoquan Ma,Weidong Luo,Li Liu,Qing Gu

doi:10.3390/app9071372

Guoxing Bai, Yu Meng + Show 4 more

Open Access

https://doi.org/10.3390/app9071372

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Journal: Applied Sciences	Publication Date: Apr 1, 2019
Citations: 39	License type: CC BY 4.0

Affiliation: University of Science and Technology Beijing

Abstract

Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

Highlights

In the underground mining environment, ensuring the safety of operators is very important and difficult
By analyzing the researches about MPC, we find that the nonlinear model predictive control (NMPC) is more suitable to solve this problem [7,8,9,10,11,12,13,14,15,16]
For path tracking control methods, the researches of Nayl et al prove that MPC is very useful

Summary

Introduction

In the underground mining environment, ensuring the safety of operators is very important and difficult. Studies of the path tracking of articulated vehicles can prove that the system constraint is not considered in control methods other than MPC. In the work of Dekker et al, the difference in the articulated angle speed between the actual vehicle and the model is clearly caused by system constraints [24]. In contrary, when longitudinal velocity is fast,that the reliability of reliability errors low, 2b These characteristics make that eNayl et al.’s controller not goodinenough the longitudinal velocity is fast.

Predicted error futureerrors errorswhen when longitudinal velocity changes:

Controller

Simulation

Conclusions