A Missing Data Approach to Data-Driven Filtering and Control

In filtering, control, and other mathematical engineering areas, it is common to use a model-based approach, which splits the problem into two steps: 1) model identification and 2) model-based design. Despite its success, the model-based approach has the shortcoming that the design objective is not...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 62; H. 4; S. 1972 - 1978
1. Verfasser: Markovsky, Ivan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2017
Schlagworte:
Adaptation models
Behavioral approach
Data models
data-driven filtering
Estimation
Linear systems
Mathematical model
missing data
Predictive models
structured low-rank approximation
system identification
Trajectory
ISSN:0018-9286, 1558-2523
Online-Zugang:Volltext
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Zusammenfassung:In filtering, control, and other mathematical engineering areas, it is common to use a model-based approach, which splits the problem into two steps: 1) model identification and 2) model-based design. Despite its success, the model-based approach has the shortcoming that the design objective is not taken into account at the identification step, i.e., the model is not optimized for its intended use. This technical note proposes an approach for data-driven filtering and control that combines the identification and the model-based design into one joint problem. The signal of interest is modeled as a missing part of a trajectory of the data generating system. Subsequently, the missing data estimation problem is reformulated as a mosaic-Hankel structured matrix low-rank approximation/completion problem. A local optimization method, based on the variable projections principle, is then used for its numerical solution. The missing data estimation approach and the solution method proposed are illustrated on filtering and smoothing examples.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2591178