Gene

Quantum mechanics

Algorithm

Control (management)

Engineering

Mathematical physics

Robust control

Feedback loop

Control system

Control engineering

Computer security

Robustness (evolution)

Full state feedback

Stability (learning theory)

Biochemistry

Computer science

Radiative transfer

Physics

Trajectory

Parametrization (atmospheric modeling)

Mathematics

Astronomy

Image (mathematics)

Invariant (physics)

Artificial intelligence

Chemistry

Control theory (sociology)

Noise (video)

Machine learning

Feedback control

State (computer science)

Electrical engineering

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 $\mathcal{H}_\infty$-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.

Robust data-driven state-feedback design