Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions

Chen, Zeren; Guan, Wei; Guo, Jianbo; Xue, Duomei; Liu, Zhengbin; Wang, Guoqiang; Quan, Long

doi:10.1016/j.autcon.2024.105364

Automation in Construction

2024

DOI: 10.1016/j.autcon.2024.105364

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Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions

Zeren Chen,

Wei Guan,

Jianbo Guo

et al.

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2024

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Cited by 2 publications

References 28 publications

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Analysis and Optimisation of Obstacle‐Crossing Performance of Electric Shovel Based on DEM‐MBD Coupling Method

Chen,

Guan,

et al. 2024

Num Anal Meth Geomechanics

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To study and enhance the obstacle‐crossing performance of the electric shovel, an obstacle‐crossing model that employs a coupling methodology integrating the discrete element method (DEM) and multi‐body dynamics (MBD) is constructed. Secondly, the influence of grouser height (GH), track velocity (TV), slope inclination (SI) and slope height (SH) on obstacle‐crossing performance is investigated through DEM‐MBD simulation, with the objective of obtaining an obstacle‐crossing surrogate model through the Kriging method and Box‐Behnken experimental design. On this basis, two optimisation solutions for the obstacle‐crossing performance of the electric shovel are proposed based on a genetic algorithm (GA), and the corresponding obstacle‐crossing performances are analysed. The results demonstrate that the coupling effect between SI and SH exerts a considerable influence on the ground pressure coefficient (GPC), power and disturbance potential energy (DPE). When the optimal TV and GH are set at 0.1 m/s and 9.38 mm, the GPC, power and disturbance kinetic energy (DKE) are observed to diminish to varying degrees, thereby indicating that the obstacle‐crossing performance of the electric shovel has been enhanced.

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Analysis and Optimisation of Obstacle‐Crossing Performance of Electric Shovel Based on DEM‐MBD Coupling Method

Chen,

Guan,

et al. 2024

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

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Research on autonomous path planning and tracking control methods for unmanned electric shovels

Tan,

Wang,

et al. 2024

Computer aided Civil Eng

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Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.

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Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions

Cited by 2 publications

References 28 publications

Analysis and Optimisation of Obstacle‐Crossing Performance of Electric Shovel Based on DEM‐MBD Coupling Method

Analysis and Optimisation of Obstacle‐Crossing Performance of Electric Shovel Based on DEM‐MBD Coupling Method

Research on autonomous path planning and tracking control methods for unmanned electric shovels

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