Adaptive-Neuro-Fuzzy-Based Information Fusion for the Attitude Prediction of TBMs

In a tunneling boring machine (TBM), to obtain the attitude in real time is very important for a driver. However, the current laser targeting system has a large delay before obtaining the attitude. So, an adaptive-neuro-fuzzy-based information fusion method is proposed to predict the attitude of a laser targeting system in real time. In the proposed method, a dual-rate information fusion is used to fuse the information of a laser targeting system and a two-axis inclinometer, and then obtain roll and pitch angles with a higher rate and provide a smoother attitude prediction. Considering that a measurement error exists, the adaptive neuro-fuzzy inference system (ANFIS) is proposed to model the measurement error, and then the ANFIS-based model is combined with the dual-rate information fusion to achieve high performance. Experimental results show the ANFIS-based information fusion can provide higher real-time performance and accuracy of the attitude prediction. Experimental results also verify that the ANFIS-based information fusion can solve the problem of the laser targeting system losing signals.

[1]  Dailin Zhang,et al.  Neural-Network-Based Iterative Learning Control for Multiple Tasks , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Li Li,et al.  Enhanced kalman-filtering iterative learning control with application to a wafer scanner , 2020, Inf. Sci..

[3]  Hongwei Zhu,et al.  A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing , 2006, Soft Comput..

[4]  Baihai Zhang,et al.  A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model , 2020, Sensors.

[5]  Bin Li,et al.  Multi-sensor fusion methodology for enhanced land vehicle positioning , 2019, Inf. Fusion.

[6]  Lei Zhu,et al.  Multi-Sensor Fusion and Error Compensation of Attitude Measurement System for Shaft Boring Machine , 2019, Sensors.

[7]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Didier Dubois,et al.  Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification , 2001, Annals of Mathematics and Artificial Intelligence.

[9]  MengChu Zhou,et al.  Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon , 2017, IEEE/CAA Journal of Automatica Sinica.

[10]  Johann Sienz,et al.  An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion , 2019, Sensors.

[11]  Jianrong Tan,et al.  Prediction of geological conditions for a tunnel boring machine using big operational data , 2019, Automation in Construction.

[12]  Xin Liu,et al.  An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter , 2019, Sensors.

[13]  F Geoffrey.  Sewers: Replacement and New Construction , 2004 .

[14]  Wei Sun,et al.  Geology prediction based on operation data of TBM: comparison between deep neural network and soft computing methods , 2018, 2019 1st International Conference on Industrial Artificial Intelligence (IAI).

[15]  Stephen Steffes.  Computationally Distributed Real-Time Dual Rate Kalman Filter , 2014 .

[16]  Bin Yao,et al.  Dynamic modeling of gripper type hard rock tunnel boring machine , 2018 .

[17]  Wu Chen,et al.  Tunnel-Boring Machine Positioning during Microtunneling Operations through Integrating Automated Data Collection with Real-Time Computing , 2011 .

[18]  Gongmin Yan,et al.  A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS , 2020, Sensors.

[19]  Tianrui Li,et al.  Dynamical Information Fusion of Multisource Incomplete Hybrid Information Systems Based on Conditional Entropy , 2019, 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[20]  Luke Fletcher,et al.  An adaptive fusion architecture for target tracking , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[21]  Mohamed Y. Hegab,et al.  Delay Time Analysis in Microtunneling Projects , 2007 .

[22]  Zhenyu Liu,et al.  Predicting the Performance of Tunnel Boring Machines using Big Operational Data , 2020, 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService).

[23]  Zhenyu Liu,et al.  TBM performance prediction with Bayesian optimization and automated machine learning , 2020 .

[24]  Wassim Khalaf,et al.  Artificial Intelligence Based Methods for Accuracy Improvement of Integrated Navigation Systems During GNSS Signal Outages: An Analytical Overview , 2020, Gyroscopy and Navigation.

[25]  Peng Fangfang,et al.  Distributed Fusion Estimation for Multisensor Multirate Systems with Stochastic Observation Multiplicative Noises , 2014 .

[26]  Angelo M. Sabatini,et al.  Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing , 2006, IEEE Transactions on Biomedical Engineering.

[27]  Jingcheng Wang,et al.  Trajectory tracking of hard rock tunnel boring machine with cascade control structure , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[28]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[29]  Jamal Rostami,et al.  Evaluating D&B and TBM tunnelling using NTNU prediction models , 2016 .

[30]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[31]  Wei Sun,et al.  Hierarchical modeling method and dynamic characteristics of cutter head driving system in tunneling boring machine , 2016 .

[32]  Mădălin-Dorin Pop,et al.  Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration , 2020, Sensors.

[33]  Junjie Huang,et al.  Optimization Modeling for Attitude Measurement of a Tunnel Boring Machine , 2011 .

[34]  Meiling Wang,et al.  Observability Analysis and Adaptive Information Fusion for Integrated Navigation of Unmanned Ground Vehicles , 2020, IEEE Transactions on Industrial Electronics.

[35]  Seong Young Ko,et al.  6-DOF force feedback control of robot-assisted bone fracture reduction system using double F/T sensors and adjustable admittances to protect bones against damage , 2016 .

[36]  Wim Desmet,et al.  State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach , 2020, Sensors.

[37]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[38]  He Li,et al.  Vibration suppression of tunnel boring machines using non-resonance approach , 2020 .

[39]  Yuriy S. Shmaliy,et al.  Indoor INS / UWB-based human localization with missing data utilizing predictive UFIR filtering , 2019, IEEE/CAA Journal of Automatica Sinica.

[40]  Zhe Chen,et al.  Progressive LiDAR adaptation for road detection , 2019, IEEE/CAA Journal of Automatica Sinica.

[41]  Ammar Assad,et al.  Novel Adaptive Fuzzy Extended Kalman Filter for Attitude Estimation in Gps-Denied Environment , 2019, Gyroscopy and Navigation.

[42]  Masayoshi Tomizuka,et al.  A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control , 2019, Sensors.

[43]  B.W. Bequette,et al.  A Dual-Rate Kalman Filter for Continuous Glucose Monitoring , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[44]  Yang Liu,et al.  An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data , 2019, Sensors.

[45]  Jie Chen,et al.  Improvement of DS Evidence Theory for Multi-Sensor Conflicting Information , 2017, Symmetry.

[46]  Masoud Monjezi,et al.  TBM performance estimation using a classification and regression tree (CART) technique , 2018, Bulletin of Engineering Geology and the Environment.