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Volume 13
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10.3390/machines13020165
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Article

Research on Operation Trajectory Tracking Control of Loader Working Mechanisms

by
Guodong Liang
1,*,
Yong Jiang
1,
Zeyu Gao
1,
Guoxing Bai
2,
Hengtong Li
1,
Xiaoyan Zhao
1,
Kai Wang
1 and
Zhiyan Wang
1
1
BGRIMM Machinery & Automation Technology Co., Ltd. (BGRIMM Technology Group), Building 23, Zone 18 of ABP, No.188, South 4th Ring Road West, Beijing 100160, China
2
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Machines 2025, 13(2), 165; https://doi.org/10.3390/machines13020165
Submission received: 6 January 2025 / Revised: 10 February 2025 / Accepted: 14 February 2025 / Published: 19 February 2025
(This article belongs to the Section Automation and Control Systems)
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Abstract

Autonomous shovel digging of loaders is the key technology to realise automation and intelligent operation. The effective tracking control for the target operation trajectory is one of its core parts. Proportional–integral–derivative (PID) and other control methods without system models have issues, such as large overshoot amplitudes and jitter phenomena under system constraints. Given that model predictive control (MPC) effectively deals with system constraints to ensure smooth operation, this paper introduces MPC into motion control for the loader’s working mechanism and proposes a trajectory tracking control method based on nonlinear model predictive control (NMPC). This study shows that, under the same system constraints for different target operation trajectories, the designed controller achieves better tracking performance than conventional PID and sliding-mode control (SMC) controllers in handling system constraints and ensuring smoothness. It is also found that the tracking performance decreases as the dig insertion depth increases. Therefore, trajectories with larger dig insertion depths are not recommended as viable operation trajectories. This study provides an important foundation and new insights for improving the control performance of the loader’s working mechanism.
Keywords: loader; working mechanism; proportional–integral–derivative (PID); sliding-mode control (SMC); nonlinear model predictive control (NMPC) loader; working mechanism; proportional–integral–derivative (PID); sliding-mode control (SMC); nonlinear model predictive control (NMPC)

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MDPI and ACS Style

Liang, G.; Jiang, Y.; Gao, Z.; Bai, G.; Li, H.; Zhao, X.; Wang, K.; Wang, Z. Research on Operation Trajectory Tracking Control of Loader Working Mechanisms. Machines 2025, 13, 165. https://doi.org/10.3390/machines13020165

AMA Style

Liang G, Jiang Y, Gao Z, Bai G, Li H, Zhao X, Wang K, Wang Z. Research on Operation Trajectory Tracking Control of Loader Working Mechanisms. Machines. 2025; 13(2):165. https://doi.org/10.3390/machines13020165

Chicago/Turabian Style

Liang, Guodong, Yong Jiang, Zeyu Gao, Guoxing Bai, Hengtong Li, Xiaoyan Zhao, Kai Wang, and Zhiyan Wang. 2025. "Research on Operation Trajectory Tracking Control of Loader Working Mechanisms" Machines 13, no. 2: 165. https://doi.org/10.3390/machines13020165

APA Style

Liang, G., Jiang, Y., Gao, Z., Bai, G., Li, H., Zhao, X., Wang, K., & Wang, Z. (2025). Research on Operation Trajectory Tracking Control of Loader Working Mechanisms. Machines, 13(2), 165. https://doi.org/10.3390/machines13020165

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