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A new likelihood approach to autonomous multiple model estimation
Date
2020-04-01
Author
Söken, Halil Ersin
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This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules. The numerical results show that the proposed algorithm, so called Competing AMM (CAMM), guarantees both steady-state estimation accuracy and quick parameter tracking. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
Subject Keywords
Instrumentation
,
Electrical and Electronic Engineering
,
Applied Mathematics
,
Computer Science Applications
URI
https://hdl.handle.net/11511/69729
Journal
ISA TRANSACTIONS
DOI
https://doi.org/10.1016/j.isatra.2019.09.005
Collections
Department of Aerospace Engineering, Article
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H. E. Söken, “A new likelihood approach to autonomous multiple model estimation,”
ISA TRANSACTIONS
, pp. 50–58, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69729.