A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots
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Department of Control Engineering and Information Technology, University of Dunaújváros, Táncsics Mihály u. 1, 2400 Dunaújváros, Hungary
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Technical Department, Faculty of Engineering, University of Szeged, Moszkvai krt. 9, 6725 Szeged, Hungary
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Department of Environmental Engineering, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány út 2, 7624 Pécs, Hungary
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Institute of Physiology, Medical School, University of Pécs, Szigeti út 12, 7624 Pécs, Hungary
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 803; https://doi.org/10.3390/s20030803
Received: 21 December 2019
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Revised: 24 January 2020
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Accepted: 29 January 2020
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Published: 1 February 2020
(This article belongs to the Special Issue Inertial Sensors)
This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.
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Keywords:
adaptive filter; attitude estimation; fuzzy logic; inertial measurement unit; extended Kalman filter; sensor fusion
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MDPI and ACS Style
Odry, Á.; Kecskes, I.; Sarcevic, P.; Vizvari, Z.; Toth, A.; Odry, P. A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots. Sensors 2020, 20, 803. https://doi.org/10.3390/s20030803
AMA Style
Odry Á, Kecskes I, Sarcevic P, Vizvari Z, Toth A, Odry P. A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots. Sensors. 2020; 20(3):803. https://doi.org/10.3390/s20030803
Chicago/Turabian StyleOdry, Ákos, Istvan Kecskes, Peter Sarcevic, Zoltan Vizvari, Attila Toth, and Péter Odry. 2020. "A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots" Sensors 20, no. 3: 803. https://doi.org/10.3390/s20030803
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