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A Cascaded Pushing Displacement Estimation Approach for Hydraulic Powered Roof Support based on Multi-Segmental Kalman Filter


Affiliations
1 School of Mechanical and Electrical Engineering, Yangtze Normal University, Chongqing, 408100, China
2 School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
3 College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, China
 

To tackle the problem of non-reusability of the magnetostrictive displacement sensor which is embedded in a pushing hydraulic cylinder, and improve the accuracy of pushing displacement sensing for hydraulic powered roof support, the compact self-contained inertial sensor is utilized in pushing displacement measurement. The motion characteristics of pushing operation are re-considered, and multi-segmental Kalman filter (MS-KF) is proposed based on the motion characteristics. A cascaded framework is constructed for pushing displacement estimation, and key technologies such as orientation estimation, segmental recognition and MS-KF implementation are demonstrated. The experiment is elaborated and experimental results show that the proposed approach significantly reduces the cumulative error and proves to be practical and valuable.

Keywords

Inertial Sensor, Hydraulic Support, Pushing Displacement, Kalman Filter.
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  • Jeon, J. and Lee, H., Development of displacement estimation method of girder bridges using measured strain signal induced by vehicular loads. Eng. Struct., 2019, 186, 203–215.
  • Pfister, S. T., Kriechbaum, K. L., Roumeliotis, S. I. and Burdick, J. W., Weighted range sensor matching algorithms for mobile robot displacement estimation. In Proceedings 2002 IEEE International Conference on Robotics and Automation, Washington DC, USA, 7 August 2002, pp. 1667–1674.
  • Slimi, T., Moussa, I. M., Kraiem, T. and Mahjoubi, H., Improvement of displacement estimation of breast tissue in ultrasound elastography using the monogenic signal. BioMed. Eng., 2017, 16(1), 19.
  • Yoon, H., Shin, J. and Spencer, B. F., Structural displacement measurement using an unmanned aerial system. Comput.-Aided Civ. Infrastruct. Eng., 2018, 33(3), 183–192.
  • Soman, R., Kyriakides, M., Onoufriou, T. and Ostachowicz, W., Numerical evaluation of multi-metric data fusion based structural health monitoring of long span bridge structures. Struct. Infrastruct. Eng., 2018, 14(6), 673–684.
  • Xu, Y. and Brownjohn, J. M. W., Review of machine-vision based methodologies for displacement measurement in civil structures. J. Civil Struct. Health Monit., 2018, 8, 91–110.
  • Torresan, C. et al., Forestry applications of UAVs in Europe: a review. Int. J. Remote Sensing, 2017, 38(8–10), 2427–2447.
  • Pierzchała, M., Talbot, B. and Astrup, R., Estimating soil displacement from timber extraction trails in steep terrain: application of an unmanned aircraft for 3D modelling. Forests, 2014, 5(6), 1212–1223.
  • Rizzello, G., Naso, D., York, A. and Seelecke, S., Closed loop control of dielectric elastomer actuators based on self-sensing displacement feedback. Smart Mater. Struct., 2016, 25(3), 35034.
  • Rizzello, G., Naso, D., York, A. and Seelecke, S., A self-sensing approach for dielectric elastomer actuators based on online estimation algorithms. IEEE/ASME Trans. Mechatronics, 2017, 22(2), 728–738.
  • Rizzello, G., Fugaro, F., Naso, D. and Seelecke, S., Simultaneous Self-sensing of displacement and force for soft dielectric elastomer actuators. IEEE Robot. Autom. Lett., 2018, 3(2), 1230–1236.
  • Tessler, A., Roy, R., Esposito, M., Surace, C. and Gherlone, M., Shape sensing of plate and shell structures undergoing large displacements using the inverse finite element method. Shock Vibra., 2018, 2018, 1–8.
  • Golemati, S., Gastounioti, A. and Nikita, K. S., Ultrasound-imagebased cardiovascular tissue motion estimation. IEEE Rev. Biomed. Eng., 2016, 9, 208–218.
  • Mirzaei, M., Asif, A., Fortin, M. and Rivaza, H., Spatio-temporal normalized cross-correlation for estimation of the displacement field in ultrasound elastography, arXiv preprint. 2018, pp. 1804– 5305.
  • Pohlman, R. M. et al., Comparison of displacement tracking algorithms for in vivo electrode displacement elastography. Ultrasound Med. Biol., 2019, 45(1), 218–232.
  • Aqel, M. O. A., Marhaban, M. H., Saripan, M. I. and Ismail, N. B., Review of visual odometry: types, approaches, challenges, and applications. SpringerPlus, 2016, 5(1).
  • Ryu, J. H., Gankhuyag, G. and Chong, K. T., Navigation system heading and position accuracy improvement through GPS and INS data fusion. J. Sensors, 2016, 2016.
  • Berrabah, S. A., Sahli, H. and Baudoin, Y., Visual-based simultaneous localization and mapping and global positioning system correction for geo-localization of a mobile robot. Meas. Sci. Technol., 2011, 22, 124003–124003.
  • Olivares, A., Górriz, J. M., Ramírez, J. and Olivares, G., Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering. Meas. Sci. Technol., 2010, 22, 025801–025801.
  • Nam, K., Lee, S., Kuc, T. and Kim, H., Position and velocity estimation for two-inertia system with nonlinear stiffness-based on acceleration sensor. Sensors (Basel, Switzerland), 2015, 16.
  • Cakmak, F., Uslu, E., Yavuz, S., Amasyali, M. F., Balcilar, M. and Altuntas, N., Using range and inertia sensors for trajectory and pose estimation. In Signal Processing and Communications Applications Conference, Trabzon, Turkey, 12 June 2014, pp. 506–509.
  • Gao, J., Webb, P. and Gindy, N., Investigation of an inertialsensorbased dynamic position measurement system for a parallel kinematic machine. Trans. Inst. Meas. Control, 2004, 26, 293– 310.
  • Zhao, H. and Wang, Z., Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended Kalman filter for data fusion. IEEE Sensors J., 2012, 12, 943–953.
  • Coyte, J. L., Stirling, D., Ros, M., Du, H. and Gray, A., Displacement profile estimation using low cost inertial motion sensors with applications to sporting and rehabilitation exercises. In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, Wollongong, NSW, Australia, 2013, pp. 1290–1295.
  • Kalman, R. E., A new approach to linear filtering and prediction problems. ASME Trans., J. Basic Eng., 1960, 82, 35–45.
  • Deng, Z. A., Hu, Y., Yu, J. G. and Na, Z. Y., Extended Kalman filter for real time indoor localization by fusing WiFi and smartphone inertial sensors. Micromachines, 2015, 6, 523–543.
  • Urrea, C. and Munoz, R., Joints position estimation of a redundant SCARA robot by means of the unscented Kalman filter and inertial sensors. Asian J. Control, 2016, 18, 481–493.
  • Liu, L. J., Qi, B., Cheng, S. M. and Xi, Z. R., High precision estimation of inertial rotation via the extended Kalman filter. Eur. Phys. J. D, 2015, 69, 1–6.
  • Qin, F., Chang, L., Jiang, S. and Zha, F., A sequential multiplicative extended Kalman filter for attitude estimation using vector observations. Sensors, 2018, 18(5), 1414.
  • Deng, F., Yang, H. and Wang, L., Adaptive unscented Kalman filter based estimation and filtering for dynamic positioning with model uncertainties. Int. J. Control, Automat. Syst., 2019, 17(3), 667–678.
  • Song, E., Xu, J. and Zhu, Y., Optimal distributed Kalman filtering fusion with singular covariance of filtering errors and measurement noises. IEEE Trans. Autom. Control, 2014, 59(5), 1271– 1282.
  • Zhang, P., Qi, W. and Deng, Z., Parallel covariance intersection fusion optimal Kalman filter. Appl. Mech. Mater., 2014, 475–476.
  • Beravs, T., Begus, S., Podobnik, J. and Munih, M., Magnetometer calibration using Kalman filter covariance matrix for online estimation of magnetic field orientation. IEEE Trans. Instrument. Meas., 2014, 63(8), 2013–2020.
  • Lee, G. B., A fast moving object tracking method by the combination of covariance matrix and Kalman filter algorithm. J. Korea Inst. Inform. Commun. Eng., 2015, 19(6), 1477–1484.
  • Wang, X., You, Z. and Zhao, K., Inertial/celestial-based fuzzy adaptive unscented Kalman filter with covariance intersection algorithm for satellite attitude determination. Aerosp. Sci. Technol., 2016, 48, 214–222.
  • Zhang, L., Wang, Z., Tan, C., Si, L., Liu, X. and Feng, S., A fruit fly-optimized Kalman filter algorithm for pushing distance estimation of a hydraulic powered roof support through tuning covariance. Appl. Sci., 2016, 6(10), 299.
  • Gandomi, A. H. and Alavi, A. H., Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul., 2012, 17(12), 4831–4845.
  • Wang, G., Gandomi, A. H., Alavi, A. H. and Deb, S., A multistage krill herd algorithm for global numerical optimization. Int. J. Artif. Intel. Tools, 2015, 1–17.
  • Wang, G., Deb, S., Gandomi, A. H. and Alavi, A. H., Oppositionbased krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing, 2016, 177(C), 147–157.
  • Wang, G., Gandomi, A. H. and Alavi, A. H., An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl. Math. Model., 2014, 38(9), 2454–2462.
  • Guo, L., Wang, G., Gandomi, A. H., Alavi, A. H. and Duan, H., A new improved krill herd algorithm for global numerical optimization. Neurocomputing, 2014, 138(2), 392–402.
  • Wang, G., Gandomi, A. H., Alavi, A. H. and Deb, S., A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput. Appl., 2016, 27(4), 989–1006.
  • Wang, G., Gandomi, A. H., Yang, X. and Alavi, A. H., A new hybrid method based on krill herd and cuckoo search for global optimization tasks. Int. J. Bio-Inspired Comput., 2016, 8(5), 286– 298.
  • Wang, G., Deb, S. and Cui, Z., Monarch butterfly optimization. Neural Comput. Appl., 2015, 1–20.
  • Wang, G., Deb, S., Zhao, X. and Cui, Z., A new monarch butterfly optimization with an improved crossover operator. Oper. Res., 2016, 1–25.
  • Wang, G., Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet. Comput., 2018, 10(2), 151–164.
  • Wang, G. G., Deb, S. and Coelho, L. D. S., Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Int. J. Bio-Inspired Comput., 2015, 12(1), 1–22.
  • Wang, G., Gandomi, A. H., Zhao, X. and Chu, H. C. E., Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput., 2016, 20(1), 273–285.
  • Wang, G., Chu, H. E. and Mirjalili, S., Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp. Sci. Technol., 2016, 49, 231–238.
  • Wang, G., Guo, L., Duan, H. and Wang, H., A new improved firefly algorithm for global numerical optimization. J. Comput. Theoret. Nanosci., 2014, 11(2), 477–485.

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  • A Cascaded Pushing Displacement Estimation Approach for Hydraulic Powered Roof Support based on Multi-Segmental Kalman Filter

Abstract Views: 326  |  PDF Views: 73

Authors

Lin Zhang
School of Mechanical and Electrical Engineering, Yangtze Normal University, Chongqing, 408100, China
Shang Feng
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Minzhou Luo
College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, China
Aimin Ji
College of Mechanical & Electrical Engineering, Hohai University, Changzhou 213022, China

Abstract


To tackle the problem of non-reusability of the magnetostrictive displacement sensor which is embedded in a pushing hydraulic cylinder, and improve the accuracy of pushing displacement sensing for hydraulic powered roof support, the compact self-contained inertial sensor is utilized in pushing displacement measurement. The motion characteristics of pushing operation are re-considered, and multi-segmental Kalman filter (MS-KF) is proposed based on the motion characteristics. A cascaded framework is constructed for pushing displacement estimation, and key technologies such as orientation estimation, segmental recognition and MS-KF implementation are demonstrated. The experiment is elaborated and experimental results show that the proposed approach significantly reduces the cumulative error and proves to be practical and valuable.

Keywords


Inertial Sensor, Hydraulic Support, Pushing Displacement, Kalman Filter.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi10%2F1585-1597