Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements
Abstract A robust three stage Kalman filtering problem is investigated in this article for non‐linear systems with stochastic non‐linearities, random faults and intermittent missing measurements. Therefore, a more general system model is governed in which unknown stochastic non‐linearities are consi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-04-01
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| Series: | IET Control Theory & Applications |
| Online Access: | https://doi.org/10.1049/cth2.12257 |
| _version_ | 1811256779682611200 |
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| author | Zahra Nejati Mostafa Abedi Alireza Faraji |
| author_facet | Zahra Nejati Mostafa Abedi Alireza Faraji |
| author_sort | Zahra Nejati |
| collection | DOAJ |
| description | Abstract A robust three stage Kalman filtering problem is investigated in this article for non‐linear systems with stochastic non‐linearities, random faults and intermittent missing measurements. Therefore, a more general system model is governed in which unknown stochastic non‐linearities are considered in both the system state and measurement equations. The fault terms are included by stochastic coefficients. Also, the sensor measurements occur in a random behaviour that encounters stochastic missing of data for each sensor. Both faults and unknown inputs are covered in the proposed filter. Moreover, the robust features of the developed estimator resolve the problems arising from the need for accurate models of faults and unknown inputs. Furthermore, any predetermined knowledge about the statistical characteristics of the above factors is not required. The augmented filters are mostly used to provide the fault diagnosis features, however, in the current work, this filter is decoupled to reduce the computational volume attributed to this approach. It is proved that the estimation error is ultimately bounded despite different stochastic terms and inaccurate information about the fault and unknown inputs. Finally, an illustrative example is proposed to demonstrate the effectiveness of the developed method. |
| first_indexed | 2024-04-12T17:46:25Z |
| format | Article |
| id | doaj.art-b1d02b1699dd40118aa1e1d692ca7bc3 |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-04-12T17:46:25Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-b1d02b1699dd40118aa1e1d692ca7bc32022-12-22T03:22:38ZengWileyIET Control Theory & Applications1751-86441751-86522022-04-0116767469710.1049/cth2.12257Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurementsZahra Nejati0Mostafa Abedi1Alireza Faraji2Department of Electrical and Computer Engineering University of Kashan Kashan IranFaculty of Electrical Engineering Department Shahid Beheshti university Tehran IranFaculty of Electrical and Computer Engineering Department University of Kashan Kashan IranAbstract A robust three stage Kalman filtering problem is investigated in this article for non‐linear systems with stochastic non‐linearities, random faults and intermittent missing measurements. Therefore, a more general system model is governed in which unknown stochastic non‐linearities are considered in both the system state and measurement equations. The fault terms are included by stochastic coefficients. Also, the sensor measurements occur in a random behaviour that encounters stochastic missing of data for each sensor. Both faults and unknown inputs are covered in the proposed filter. Moreover, the robust features of the developed estimator resolve the problems arising from the need for accurate models of faults and unknown inputs. Furthermore, any predetermined knowledge about the statistical characteristics of the above factors is not required. The augmented filters are mostly used to provide the fault diagnosis features, however, in the current work, this filter is decoupled to reduce the computational volume attributed to this approach. It is proved that the estimation error is ultimately bounded despite different stochastic terms and inaccurate information about the fault and unknown inputs. Finally, an illustrative example is proposed to demonstrate the effectiveness of the developed method.https://doi.org/10.1049/cth2.12257 |
| spellingShingle | Zahra Nejati Mostafa Abedi Alireza Faraji Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements IET Control Theory & Applications |
| title | Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements |
| title_full | Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements |
| title_fullStr | Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements |
| title_full_unstemmed | Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements |
| title_short | Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements |
| title_sort | modified three stage kalman filtering for stochastic non linear systems with randomly occurring faults and intermittent measurements |
| url | https://doi.org/10.1049/cth2.12257 |
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