2019
DOI: 10.1186/s10033-019-0347-5
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Indirect Adaptive Robust Trajectory Tracking Control of Hard Rock TBM with Load Variation of Tunneling Face

Abstract: Posture adjustment of open-type hard rock tunnel boring machine (TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper, the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters an… Show more

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Cited by 6 publications
(3 citation statements)
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“…During the excavation process, gripper shoes support the rock, relying on the output force of the gripper cylinder to provide the counter-force and counter-torque needed for excavating, and Figure 2 shows the working state of an gripper cylinder [25].…”
Section: Configuration Of the Support Hydraulic Systemmentioning
confidence: 99%
“…During the excavation process, gripper shoes support the rock, relying on the output force of the gripper cylinder to provide the counter-force and counter-torque needed for excavating, and Figure 2 shows the working state of an gripper cylinder [25].…”
Section: Configuration Of the Support Hydraulic Systemmentioning
confidence: 99%
“…For theoretical methods, in consideration of the effective stress and the seepage force, the limit support pressure, which is calculated by the limit equilibrium method, is required to stabilize the stability of the tunnel face (Wang et al, 2019;Wu et al, 2021). Simultaneously, the limit equilibrium method was also used to analyze how both the hydraulic heterogeneity and the advance drainage influence the stability of tunnel face (Li et al, 2020;Su, 2019;Zingg and Anagnostou, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…The first category focuses on controlling the tunnelling direction of the TBM by adjusting the tunnelling parameters, primarily the stroke of thrust and gripper cylinders. For example, Shao et al built a three-dimensional tunnelling directioncontrolling dynamic model which was used to adjust the tunnelling direction according to the need by controlling the thrust and gripper cylinders [5,6]. Zhou et al used a hybrid method including a convolutional neural network (CNN) and a long-term and shortterm memory network (LSTM) to establish a relationship model with multiple tunnelling parameters as the input and the horizontal and vertical tunnelling attitude as the output, and this model can be used to predict the TBM tunnelling direction [7].…”
Section: Introductionmentioning
confidence: 99%