Open access
Date
2023-11-22Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Kalman and H∞ filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures are a promising alternative, as they aim to adapt to recognizable patterns in the estimation error. In this paper, we show that the regret minimization problem for finite horizon estimation can be cast into a simple convex optimization problem. For this purpose, we first rewrite linear time-varying system dynamics using a novel system level synthesis parametrization for state estimation, capable of handling both disturbance and measurement noise. We then provide a tractable formulation for the minimization of regret based on semi-definite programming. Both contributions make the minimal regret observer design easily implementable in practice. Finally, numerical experiments show that the computed observer can significantly outperform both H₂ and H∞ filters. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000660769Publication status
publishedExternal links
Journal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
minimal regret; observer design; state estimationFunding
180545 - NCCR Automation (phase I) (SNF)
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ETH Bibliography
yes
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