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Title: Worst-Case Optimal Data-Driven Estimators for Switched Discrete-Time Linear Systems
This paper proposes a data-driven framework to address the worst-case estimation problem for switched discrete-time linear systems based solely on the measured data (input & output) and an ℓ ∞ bound over the noise. We start with the problem of designing a worst-case optimal estimator for a single system and show that this problem can be recast as a rank minimization problem and efficiently solved using standard relaxations of rank. Then we extend these results to the switched case. Our main result shows that, when the mode variable is known, the problem can be solved proceeding in a similar manner. To address the case where the mode variable is unmeasurable, we impose the hybrid decoupling constraint(HDC) in order to reformulate the original problem as a polynomial optimization which can be reduced to a tractable convex optimization using moments-based techniques.
Authors:
;
Award ID(s):
1638234 1808381 1814631 1646121
Publication Date:
NSF-PAR ID:
10176074
Journal Name:
2019 IEEE 58th Conference on Decision and Control (CDC)
Page Range or eLocation-ID:
3417 to 3422
Sponsoring Org:
National Science Foundation
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