A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Autonomous steering control for tunnel boring machines
Abstract Driven by the growing requirements for long-distance tunnels, autonomous steering control is a productive research to surpass the limitations of manual operation in steering control of tunnel boring machines (TBMs). However, existing reviews have not yet systematically discussed it from the perspective of hierarchical control. Besides, they mainly focused on how to use artificial intelligence to accomplish this task, while neglecting the knowledge-based studies. Therefore, based on the definition of hierarchical control, this paper surveys a wide range of literature on autonomous steering control from three aspects: 1) Perception layer acquires information about attitude deviation, geological conditions, and shield tail clearance; 2) Decision layer gives appropriate commands for thrust system, making the actual tunnel axis approach the designed one; 3) Execution layer designs feedback-based controllers to ensure the hydraulic cylinders accurately implement those commands. Ultimately, some future directions are highlighted to improve the efficiency and stability of tunneling.
Graphical abstract Display Omitted
Highlights Autonomous steering control for TBM is discussed from the perception, decision, and execution layers. Perception layer discusses how to acquire attitude deviation, geological conditions, and shield tail clearance. Decision layer concerns with giving appropriate commands to thrust system by data-driven or knowledge analysis. Execution layer pays attention to the control algorithms to achieve accurate tracking of the preset trajectories.
Autonomous steering control for tunnel boring machines
Abstract Driven by the growing requirements for long-distance tunnels, autonomous steering control is a productive research to surpass the limitations of manual operation in steering control of tunnel boring machines (TBMs). However, existing reviews have not yet systematically discussed it from the perspective of hierarchical control. Besides, they mainly focused on how to use artificial intelligence to accomplish this task, while neglecting the knowledge-based studies. Therefore, based on the definition of hierarchical control, this paper surveys a wide range of literature on autonomous steering control from three aspects: 1) Perception layer acquires information about attitude deviation, geological conditions, and shield tail clearance; 2) Decision layer gives appropriate commands for thrust system, making the actual tunnel axis approach the designed one; 3) Execution layer designs feedback-based controllers to ensure the hydraulic cylinders accurately implement those commands. Ultimately, some future directions are highlighted to improve the efficiency and stability of tunneling.
Graphical abstract Display Omitted
Highlights Autonomous steering control for TBM is discussed from the perception, decision, and execution layers. Perception layer discusses how to acquire attitude deviation, geological conditions, and shield tail clearance. Decision layer concerns with giving appropriate commands to thrust system by data-driven or knowledge analysis. Execution layer pays attention to the control algorithms to achieve accurate tracking of the preset trajectories.
Autonomous steering control for tunnel boring machines
Zheng, Zhe (author) / Luo, Kaidi (author) / Tan, Xianzhong (author) / Jia, Lianhui (author) / Xie, Mingrui (author) / Xie, Haibo (author) / Jiang, Lijie (author) / Gong, Guofang (author) / Yang, Huayong (author) / Han, Dong (author)
2023-12-27
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2003
| Wiley | 2012
| Online Contents | 2003
Hardrock tunnel boring machines
| UB Braunschweig | 2008
Hardrock tunnel boring machines
| TIBKAT | 2008