SCA-MADRL: Multiagent deep reinforcement learning framework based on state classification and assignment for intelligent shield attitude control

Xu, Jin; Bu, Jinfeng; Qin, Na; Huang, Deqing

doi:10.1016/j.eswa.2023.121258

Cited by 7 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the present stage, the control mode of shield machine is mainly based on manual experience adjustment when facing the sudden change of geological conditions and abnormal working conditions, it is difficult for the operator to correct the position trajectory in time and it is very easy to cause the position trajectory to deviate from the axis [1,2]. Therefore, it is necessary to realize the intelligent real-time prediction for shield machine position parameters [3,4], which will help the shield machine driver to make accurate regulations, effectively avoid the shield machine position offset problem, and ensure safe and efficient construction.…”

Section: Introductionmentioning

confidence: 99%

Intelligent real-time prediction for shield machine position on the basis of BWO-LSTM-GRU

Xuanyu,

Mengting,

Wenshuai

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

Due to the complexity and variability of shield machine working environment, it is very important to accurately control and regulate the position trajectory of shield machine. For that reason, an intelligent real-time prediction model of shield machine position based on BWO-LSTM-GRU (Beluga whale optimization-Long Short-term Memory-Gated recurrent unit) is proposed in this paper. Firstly, the real-time data of shield machine are processed based on Pearson correlation analysis, and the tunneling parameters presenting medium-strong correlation with the position parameters are filtered to obtain, which were used to be input variables for prediction models. Secondly, LSTM-GRU position prediction model was established separately for shield machine position parameters, and four hyperparameters of the model were optimized separately using BWO. Finally, BWO-LSTM-GRU position prediction models are used to realize the intelligent real-time prediction of the motion trajectories at four positions for shield machine. The simulation results indicate that the prediction deviation in the position prediction model is within 3 mm, and it can accurately complete the task of real-time prediction, providing real-time data support for shield machine drivers.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent real-time prediction for shield machine position on the basis of BWO-LSTM-GRU

Xuanyu,

Mengting,

Wenshuai

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…In the excavation process, due to the highly complex operation of the shield machine, the shield driver only relies on experience to adjust the attitude parameters with great uncertainty, and there are cases of inadequate or untimely regulation. In this regard, in order to further ensure the accurate adjustment of shield attitude, it is very important to realize the prediction of shield attitude and position [5,6]. At this stage, predictive controlling is widely applied in shield performance [7,8] and maintaining the stability of the excavation surface [9,10], but due to the complexity of the tunneling environment, the establishment of a multi-step accurate position predictive model is still challenging research, and the realization of precise control for position still needs to be explored and practiced further.…”

Section: Introductionmentioning

confidence: 99%

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU

Liu,

Zhang,

Mengting

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.

show abstract

Automated position control of tunnel boring machine during excavation using deep reinforcement learning

Lin,

Ji,

Zhou

et al. 2024

Applied Soft Computing

View full text Add to dashboard Cite

SCA-MADRL: Multiagent deep reinforcement learning framework based on state classification and assignment for intelligent shield attitude control

Cited by 7 publications

References 34 publications

Intelligent real-time prediction for shield machine position on the basis of BWO-LSTM-GRU

Intelligent real-time prediction for shield machine position on the basis of BWO-LSTM-GRU

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU

Automated position control of tunnel boring machine during excavation using deep reinforcement learning

Contact Info

Product

Resources

About