Model predictive control

Control (management)

Linear system

Ecology

Algebraic number

Mathematical physics

Biology

Statistics

Materials science

Poaceae

Stability (learning theory)

Computer science

LTI system theory

Matrix (chemical analysis)

Mathematics

Invariant (physics)

Discrete time and continuous time

Lemma (botany)

Mathematical analysis

Artificial intelligence

Composite material

Parametric statistics

Control theory (sociology)

Machine learning

IEEE Control Systems Letters

In this letter we investigate data-driven predictive control of discrete-time linear descriptor systems. Specifically, we give a tailored variant of Willems’ fundamental lemma, which shows that for descriptor systems the non-parametric modeling via a Hankel matrix requires less data compared to linear time-invariant systems without algebraic constraints. Moreover, we use this description to propose a data-driven framework for optimal control and predictive control of discrete-time linear descriptor systems. For the latter, we provide a sufficient stability condition for receding-horizon control before we illustrate our findings with an example.

Willems’ Fundamental Lemma for Linear Descriptor Systems and Its Use for Data-Driven Output-Feedback MPC