Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

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

doi:10.3390/app9071372

Cited by 42 publications

(38 citation statements)

References 33 publications

(48 reference statements)

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“…The deviation occurs in the transient process of steering according to the time mark. The maximum error of the deviation is 0.21 m when the velocity is 2 m/s; this value is greater than the errors in some control and positioning systems used in UAVs [11,19]. The deviation decreases when the velocity increases.…”

Section: Analysis Of Steering Characteristicsmentioning

confidence: 77%

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Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

et al. 2019

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Owing to the harsh environment of underground mines, autonomous underground articulated vehicles (UAVs) with precise control and positioning system are particularly important. However, the ambiguity of steering characteristics hinders the development of UAVs. This study presents a model-based method to uncover the steering characteristics of a UAV. Firstly, a hybrid model of UAV was established, which included a dynamic model of articulated frames and a model of the hydraulic power steering system. Secondly, a field test of a typical UAV, a load-haul-dump (LHD) with 4 m3 capacity, was carried out. In order to verify the correctness of the established model and the accuracy of the involved parameters, the field test results were used to verify the dynamic model in time and frequency domains. Then, the steering characteristics of the UAV were uncovered based on the verified hybrid model, and the results showed that the increased load would increase ‘oversteering’ under the same articulation angle and that the error of trajectory exceeded 0.3 m. In addition, the deviations of trajectories between the two frames were revealed during the transient steering process, and the maximum deviation reached 0.21 m when the velocity was 2 m/s and the articulation angle was 15°. The comprehensive results indicate that the steering characteristics of UAVs cannot be ignored in regard to precise autonomous control and positioning.

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Section: Analysis Of Steering Characteristicsmentioning

confidence: 77%

“…Based on the instability results, an Ackman-based hierarchical controller was designed to improve the stability of UAVs. Bai [19,20] et al designed a nonlinear model predictive controller based on the kinematic model of a UAV, with a maximum error of 0.14 m.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

et al. 2019

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“…However, in previous studies, we found that MPC is not perfect. Like feedback linearization and LQR, Nayl et al's switching MPC and our nonlinear MPC also cannot adjust the velocity in a wide range [5]. However, the accuracy of the path tracking control can be greatly improved by reducing the velocity during steering.…”

Section: Related Workmentioning

confidence: 97%

“…The motivation for this paper is to further improve the accuracy of path tracking. In previous work, we found that most path tracking methods cannot adjust the velocity of the vehicle according to path information such as the radius of the curve, and a high reference velocity at corners will lead to a sharp deterioration in path tracking performance [5].…”

Section: Motivationsmentioning

confidence: 99%

“…Its task is to control the vehicles devices to track the reference path by minimizing errors [1][2][3][4]. Based on our previous work [5], this paper still focuses on the path tracking of articulated vehicles. Of course, the control method proposed in this paper can also be applied in the path tracking of mobile devices such as front-wheel steering vehicles and differential robots.…”

Section: Introductionmentioning

confidence: 99%

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A New Path Tracking Method Based on Multilayer Model Predictive Control

et al. 2019

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At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

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Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Wang

et al. 2023

Journal of Field Robotics

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This paper proposes a path tracking control method combining pure tracking algorithms and self‐adaptive fuzzy control for autonomous driving of an unmanned electric shovel. An improved pure tracking controller was designed based on the kinematic model of heavy crawler taking both the value of deviation and its variation as inputs with the crawler speed on each side as output. The proposed controller and MPC algorithm were simulated using MATLAB for comparison. The results show that the proposed controller has more anthropomorphic characteristics than the MPC method. To verify the actual control effect of the controller, experiments were carried out using a prototype electric shovel for different working conditions. The experimental results proved that the controller is able to meet the control requirements for unmanned electric shovel path tracking.

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Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

Cited by 42 publications

References 33 publications

Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

A New Path Tracking Method Based on Multilayer Model Predictive Control

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

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