Robust data-driven state-feedback design

We consider the problem of designing robust state-feedback controllers for discrete-time linear time-invariant systems, based directly on measured data. The proposed design procedures require no model knowledge, but only a single open-loop data trajectory, which may be affected by noise. First, a da...

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Bibliographic Details
Main Authors: Berberich, Julian, Koch, Anne, Scherer, Carsten W., Allgower, Frank
Format: Conference Proceeding
Language:English
Subjects:
Linear matrix inequalities
Linear systems
Mathematical model
Noise measurement
Robust control
Robustness
Trajectory
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Summary:We consider the problem of designing robust state-feedback controllers for discrete-time linear time-invariant systems, based directly on measured data. The proposed design procedures require no model knowledge, but only a single open-loop data trajectory, which may be affected by noise. First, a data-driven characterization of the uncertain class of closed-loop matrices under state-feedback is derived. By considering this parametrization in the robust control framework, we design data-driven state-feedback gains with guarantees on stability and performance, containing, e.g., the ℋ ∞ -control problem as a special case. Further, we show how the proposed framework can be extended to take partial model knowledge into account. The validity of the proposed approach is illustrated via a numerical example.
ISSN:2378-5861
DOI:10.23919/ACC45564.2020.9147320