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Jan Thore Lassen
Paolo Mercorelli



Author(s) and WSEAS

Jan Thore Lassen
Paolo Mercorelli


WSEAS Transactions on Systems and Control


Print ISSN: 1991-8763
E-ISSN: 2224-2856

Volume 14, 2019

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Volume 14, 2019



Tuning Kalman Filter in Linear Systems

AUTHORS: Jan Thore Lassen, Paolo Mercorelli

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ABSTRACT: Kalman filters are used in many different areas that require a solution to discrete-data linear filtering problems. Especially in the field of electric controls, Kalman filters represent a used approach and they are an integral part of many states of the art of electric controls. However, the practical implementation of the Kalman Filter often presents difficulties due to the challenging task of getting a good estimate of the covariance matrix of the process noise and covariance matrix of the measurement noise. A fitting and simultaneous choice of these two matrices based on a feedback loop within the Kalman filter realized by the filter itself can directly lead to an asymptotically stable operating Kalman filter after a reasonable amount of iterations. In this paper an approach to apply a feedback loop enabling dynamic values of the covariance matrix process noise and covariance matrix of the measurement noise is presented. This approach will be applied in simulations using Matlab/Simulink

KEYWORDS: Kalman Filter, Linear Systems, DC-Drives, Sensors

REFERENCES:

[1] M. Schimmack, B. Haus, and P. Mercorelli. An extended Kalman filter as an observer in a control structure for health monitoring of a metal polymer hybrid soft actuator. IEEE/ASME Transactions on Mechatronics, 23(3):1477–1487, 2018.

[2] L.Tubiana S. Bolognani and M. Zigliotto. Extended Kalman filter tuning in sensorless pmsm drives. IEEE Transactions on Industry Applications, 39(6):1741–1747, 2003.

[3] M. R. Ananthasayanam, M. Shyam Mohan, Naren Naik, and R. M. O. Gemson. A heuristic reference recursive recipe for adaptively tuning the Kalman filter statistics part-1: formulation and simulation studies. Sadhana, 41(12):1473– 1490, Dec 2016.

[4] Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid Information Services for Distributed Resource Sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181--184. IEEE Press, New York (2001)

[5] P. Mercorelli. A hysteresis hybrid extended Kalman filter as an observer for sensorless valve control in camless internal combustion engines. IEEE Transactions on Industry Applications, 48(6):1940–1949, 2012.

[6] P. Mercorelli. A two-stage sliding-mode high-gain observer to reduce uncertainties and disturbances effects for sensorless control in automotive applications. IEEE Transactions on Industrial Electronics, 62(9):5929–5940, Sept 2015.

WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 14, 2019, Art. #26, pp. 209-212


Copyright Β© 2019 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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