Abstract
In the present work we designed a neuro-fuzzy approach for on-line optimal tuning of a Kalman filter of a gyroscope within a Micro ElectroMechanical Sensor (MEMS) device. It consists of Adaptive Critic Design (ACD) scheme in which the controller (a Fuzzy Rule Base (FRB) designed to adapt the measurement noise covariance matrix of a Kalman filter) is tuned using only information about the direction to which the estimation error changes (increase or decrease). A novel fast training dynamic neural network structure - Echo state network (ESN) - was used in the role of the critic element. Application to data collected from real MEMS demonstrated the ability of the proposed approach to tune Kalman filter and improve the quality of its estimates in changing working conditions of the MEMS in real time.
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Acknowledgment
The research work reported in the paper is partly supported by the project AComIn, grant 316087, funded by the FP7 Capacity Programme (Research Potential of Convergence Regions).
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Koprinkova-Hristova, P., Alexiev, K. (2015). ACD with ESN for Tuning of MEMS Kalman Filter. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_24
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DOI: https://doi.org/10.1007/978-3-319-26520-9_24
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