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Abstrakt

The article presents the possibilities of using popular MEMS inertial sensors in the object tilt angle estimation system and in the system for stabilizing the vertical position of the balancing robot. Two research models were built to conduct the experiment. The models use microcontroller development board of the STM32F3 series with the Cortex-M4 core, equipped with a three-axis accelerometer, magnetometer and gyroscope. To determine the accuracy of the angle estimation, comparative tests with a pulse encoder were performed.
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Bibliografia

[1] P. Groves, “Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems”, Norwood, MA: Artech House, 2008.
[2] J. Collin, P. Davidson, M. Kirkko-Jaakkola, H. Leppäkoski “Inertial Sensors and Their Applications” S. Bhattacharyya, E. Deprettere, R. Leupers, J.Takala “Handbook of Signal Processing Systems”. Springer, 2019, pp.51-85. DOI 10.1007/978-3-319-91734-4_2
[3] M. Labowski, P. Kaniewski, P. Serafin, "Inertial Navigation System for Radar Terrain Imaging," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, pp. 942-948, April 2016.
[4] M. Elhoushi, J. Georgy, A. Noureldin and M. J. Korenberg, "A Survey on Approaches of Motion Mode Recognition Using Sensors," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 7, pp. 1662-1686, July 2017, DOI: 10.1109/TITS.2016.2617200.
[5] B. Aguiar, T. Rocha, J. Silva and I. Sousa, "Accelerometer-based fall detection for smartphones," 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lisboa, 2014, pp. 1-6, DOI: 10.1109/MeMeA.2014.6860110.
[6] J. M. Darmanin et al., "Development of a High-G Shock Sensor Based on MEMS Technology for Mass-Market Applications," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-4, DOI: 10.1109/ISISS.2019.8739763.
[7] M. Mansoor, I. Haneef, S. Akhtar, M. A. Rafiq, S. Z. Ali and F. Udrea, "SOI CMOS multi-sensors MEMS chip for aerospace applications," SENSORS, 2014 IEEE, Valencia, Spain, 2014, pp. 1204-1207, DOI: 10.1109/ICSENS.2014.6985225.
[8] A. Mikov, A. Panyov, V. Kosyanchuk and I. Prikhodko, "Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-2, DOI: 10.1109/ISISS.2019.8739427.
[9] C. Acar, "High-performance 6-Axis MEMS inertial sensor based on Through-Silicon Via technology," 2016 IEEE International Symposium on Inertial Sensors and Systems, Laguna Beach, CA, 2016, pp. 62-65, DOI: 10.1109/ISISS.2016.7435545.
[10] I. P. Prikhodko, B. Bearss, C. Merritt, J. Bergeron and C. Blackmer, "Towards self-navigating cars using MEMS IMU: Challenges and opportunities," 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Moltrasio, 2018, pp. 1-4, DOI: 10.1109/ISISS.2018.8358141.
[11] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems." ASME. J. Basic Eng. March 1960; vol. 82, no1, pp. 35–45. DOI 10.1115/1.3662552
[12] J. Gajda, R. Sroka, M. Stencel, T. Żegleń, “Data fusion applications in the traffic parameters measurement”, Metrology and Measurement Systems, vol. 2, no. 3, pp. 249–262, 2005.
[13] S.Chudzik, “The idea of using artificial neural network in measurement system with hot probe for testing parameters of heat-insulating materials”, Measurement, vol. 42 pp. 764–770, 2009.
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Autorzy i Afiliacje

Stanisław Chudzik
1

  1. Czestochowa University of Technology, Czestochowa, Poland

Abstrakt

The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

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Autorzy i Afiliacje

Stanisław Chudzik
Waldemar Minkina

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