Intelligent posture deviation correction control method and system for continuous tunneling shield tunneling machine
By integrating BiLSTM and KAN networks to predict the attitude of the shield machine and combining MOGWO to optimize the PID parameters, the problem of the attitude of the shield machine being out of control during the synchronous construction of push-and-assembly is solved, and construction efficiency and safety are improved.
CN120487120APending Publication Date: 2025-08-15HUAZHONG UNIV OF SCI & TECH
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Patent Information
- Application Number
- CN202510826953.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-19
- Publication Date
- 2025-08-15
AI Technical Summary
Technical Problem
The existing shield machine lacks attitude control methods when the assembly cylinder is missing during the synchronous construction of push-and-assembly, resulting in a high risk of attitude loss and affecting the safety and efficiency of tunnel construction.
Method used
The deep learning method combined with BiLSTM and KAN network is used to predict the pose of the shield machine, and the PID parameters are optimized through the MOGWO algorithm to build a multi-objective optimization framework for intelligent pose deviation control.
Benefits of technology
It improves the attitude control accuracy and construction efficiency of the shield machine under complex working conditions, reduces the risk of snaking in tunnel construction, and ensures construction safety and quality.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention belongs to the field of shield tunneling machines, and discloses an intelligent posture deviation correction control method and system for a continuous tunneling shield tunneling machine, and the method comprises the steps: carrying out a pushing and splicing synchronous construction experiment to collect data; integrating the BiLSTM and the KAN, and predicting the posture of the continuous tunneling shield tunneling machine; in combination with an MOGWO method, an optimal proportional-integral-differential (PID) parameter is determined, and attitude deviation is minimized. The invention provides a shield attitude intelligent control method integrating deep learning, physical modeling and an intelligent optimization algorithm. The key innovation point is that the KAN model and the BiLSTM model are combined to accurately predict the attitude of the continuous tunneling shield machine in various assembling hydraulic oil cylinder missing scenes. And then combining the improved BiLSTM model with an MOGWO algorithm to optimize the response performance of the propulsion hydraulic system, and finally improving the efficiency and quality of tunnel construction. Verification research is carried out by utilizing actual measurement data of a continuous tunneling shield tunneling machine test bed, and the effectiveness of the method is verified.
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