Multi-Sensor Fusion and Error Compensation of Attitude Measurement System for Shaft Boring Machine

To ensure that the shaft boring machine (SBM) runs along the pre-designed axis steadily, the role of the attitude measurement system is essential, but its accuracy and reliability cannot be guaranteed. Currently, there is no effective technology to meet the actual requirements, and there is a lack of relevant theoretical research in this field. Through further study of the attitude analysis method and multi-sensor fusion technology, this paper presents a dual coordinate method, which can be used to describe the attitude characteristics of the SBM. Moreover, this paper discusses the relationships between the attitude changes and the values of the angle as well as displacement and analyzes the implementation complexity and computational efficiency of related algorithms in software and hardware. According to the working characteristics of the SBM, the hardware design and the reasonable layout of the attitude measurement system are provided. Based on multi-sensor data, this paper puts forward an improved method combining a complementary filter with an extended Kalman filter (EKF) for attitude estimation and error compensation. The simulation experiments of different working processes verify the steady-state response and dynamic response performance of the method. Experimental results show that the dual coordinate method and the proposed filter are more suitable for attitude estimation of the SBM compared to other methods.

[1]  Chen Wenjie,et al.  Compensation for MEMS gyroscope zero bias stability , 2013, 2013 Chinese Automation Congress.

[2]  Yihua Liao,et al.  Attitude measure system based on extended Kalman filter for multi-rotors , 2017, Comput. Electron. Agric..

[3]  Nasser Sadati,et al.  Multi-model robust control of depth of hypnosis , 2018, Biomed. Signal Process. Control..

[4]  Mark Euston,et al.  A complementary filter for attitude estimation of a fixed-wing UAV , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  N. P. Futane,et al.  Angular rate error compensation of MEMS based gyroscope using artificial neural network , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[6]  Valérie Renaudin,et al.  Magnetic, Acceleration Fields and Gyroscope Quaternion (MAGYQ)-Based Attitude Estimation with Smartphone Sensors for Indoor Pedestrian Navigation , 2014, Sensors.

[7]  Yifei Han,et al.  Analysis and compensation of MEMS gyroscope drift , 2013, 2013 Seventh International Conference on Sensing Technology (ICST).

[8]  A. Pascoal,et al.  RMMAC: a novel robust adaptive control scheme. Part I. Architecture , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[9]  Holger Blume,et al.  Modification and fixed-point analysis of a Kalman filter for orientation estimation based on 9D inertial measurement unit data , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Henry J. Pernicka,et al.  Magnetometer-only Attitude Determination Using Novel Two-step Kalman Filter Approach , 2012 .

[11]  Pradeep Lall,et al.  Effect of simultaneous high temperature and vibration on MEMS based vibratory gyroscope , 2017, 2017 16th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm).

[12]  刘莉,et al.  Temperature compensation method for low cost three-axis MEMS digital angular rate gyroscopes , 2016 .

[13]  A. M. Lestev,et al.  Combination resonances in mems gyro dynamics , 2015 .

[14]  Fangjun Qin,et al.  Indirect Kalman Filtering Based Attitude Estimation for Low-Cost Attitude and Heading Reference Systems , 2017, IEEE/ASME Transactions on Mechatronics.

[15]  Ya. A. Nekrasov,et al.  Influence of translational vibrations, shocks and acoustic noise on MEMS gyro performance , 2017 .

[16]  Pietro Falco,et al.  Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation , 2014 .

[17]  Philippe Martin,et al.  Design and implementation of a low-cost observer-based attitude and heading reference system , 2010 .

[18]  Xiang Zhang,et al.  An improved Kalman filter for attitude determination of multi-rotor UAVs based on low-cost MEMS sensors , 2016, 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC).

[19]  Marco Carminati,et al.  Real-Time Data Fusion and MEMS Sensors Fault Detection in an Aircraft Emergency Attitude Unit Based on Kalman Filtering , 2012, IEEE Sensors Journal.

[20]  Jitendra R. Raol,et al.  Multi-Sensor Data Fusion with MATLAB® , 2009 .

[21]  Luca Fanucci,et al.  A Double-Stage Kalman Filter for Orientation Tracking With an Integrated Processor in 9-D IMU , 2013, IEEE Transactions on Instrumentation and Measurement.

[22]  L. Fanucci,et al.  A double stage Kalman filter for sensor fusion and orientation tracking in 9D IMU , 2012, 2012 IEEE Sensors Applications Symposium Proceedings.

[23]  Yakov A. Nekrasov,et al.  MEMS gyro vibration immunity and its measurement with TIRA shaker , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[24]  Esther Neye,et al.  Rapid shaft sinking , 2013 .

[25]  Angelo M. Sabatini,et al.  Estimating Orientation Using Magnetic and Inertial Sensors and Different Sensor Fusion Approaches: Accuracy Assessment in Manual and Locomotion Tasks , 2014, Sensors.

[26]  Zachary Wachter.  A Cost Effective Motion Platform for Performance Testing of MEMS-Based Attitude and Heading Reference Systems , 2013, J. Intell. Robotic Syst..

[27]  Huosheng Hu,et al.  Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles , 2018, Sensors.

[28]  Sheng Liu,et al.  A Dual-Linear Kalman Filter for Real-Time Orientation Determination System Using Low-Cost MEMS Sensors , 2016, Sensors.

[29]  Gintautas Palubinskas.  Framework for multi-sensor data fusion using template based matching , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[30]  Ming Lu,et al.  Virtual Laser Target Board for Alignment Control and Machine Guidance in Tunnel-Boring Operations , 2015, J. Intell. Robotic Syst..

[31]  Esther Neye,et al.  Rapid shaft sinking / Mechanisiertes Abteufen von Schächten , 2013 .

[32]  Flor Lizeth Torres Ortiz,et al.  A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors , 2013, Sensors.

[33]  Jinling Wang,et al.  Effective Adaptive Kalman Filter for MEMS-IMU/Magnetometers Integrated Attitude and Heading Reference Systems , 2012, Journal of Navigation.

[34]  Bo Hou,et al.  Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization , 2017, Sensors.

[35]  Bernhard Maidl,et al.  Handbook of Tunnel Engineering: Volume I: Structures and Methods , 2013 .

[36]  Zhenbang Gong,et al.  An Adaptive Filter for a Small Attitude and Heading Reference System Using Low Cost Sensors , 2011 .