Formulation of the Alpha Sliding Innovation Filter: A Robust Linear Estimation Strategy

In this paper, a new filter referred to as the alpha sliding innovation filter (ASIF) is presented. The sliding innovation filter (SIF) is a newly developed estimation strategy that uses innovation or measurement error as a switching hyperplane. It is a sub-optimal filter that provides a robust and...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (22), p.8927
Main Authors: AlShabi, Mohammad, Gadsden, Stephen Andrew
Format: Article
Language:English
Subjects:
Actuators
Algorithms
Error analysis
Estimates
Estimation theory
Experimentation
Filters (Mathematics)
forgetting factor
Hyperplanes
Innovations
Kalman filters
Methods
robustness
Signal processing
Sliding
sliding innovation filter
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Summary:In this paper, a new filter referred to as the alpha sliding innovation filter (ASIF) is presented. The sliding innovation filter (SIF) is a newly developed estimation strategy that uses innovation or measurement error as a switching hyperplane. It is a sub-optimal filter that provides a robust and stable estimate. In this paper, the SIF is reformulated by including a forgetting factor, which significantly improves estimation performance. The proposed ASIF is applied to several systems including a first-order thermometer, a second-order spring-mass-damper, and a third-order electrohydrostatic actuator (EHA) that was built for experimentation. The proposed ASIF provides an improvement in estimation accuracy while maintaining robustness to modeling uncertainties and disturbances.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22228927