Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
Behavioral systems theory in data-driven analysis, signal processing, and control
The behavioral approach to systems theory, put forward 40 years ago by Jan C. Willems,
takes a representation-free perspective of a dynamical system as a set of trajectories. Till …
takes a representation-free perspective of a dynamical system as a set of trajectories. Till …
Formulas for data-driven control: Stabilization, optimality, and robustness
In a paper by Willems et al., it was shown that persistently exciting data can be used to
represent the input-output behavior of a linear system. Based on this fundamental result, we …
represent the input-output behavior of a linear system. Based on this fundamental result, we …
Data-driven model predictive control with stability and robustness guarantees
We propose a robust data-driven model predictive control (MPC) scheme to control linear
time-invariant systems. The scheme uses an implicit model description based on behavioral …
time-invariant systems. The scheme uses an implicit model description based on behavioral …
Data informativity: A new perspective on data-driven analysis and control
The use of persistently exciting data has recently been popularized in the context of data-
driven analysis and control. Such data have been used to assess system-theoretic …
driven analysis and control. Such data have been used to assess system-theoretic …
Bridging direct and indirect data-driven control formulations via regularizations and relaxations
In this article, we discuss connections between sequential system identification and control
for linear time-invariant systems, often termed indirect data-driven control, as well as a …
for linear time-invariant systems, often termed indirect data-driven control, as well as a …
From noisy data to feedback controllers: Nonconservative design via a matrix S-lemma
In this article, we propose a new method to obtain feedback controllers of an unknown
dynamical system directly from noisy input/state data. The key ingredient of our design is a …
dynamical system directly from noisy input/state data. The key ingredient of our design is a …
Distributionally robust chance constrained data-enabled predictive control
In this article we study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …
An overview of systems-theoretic guarantees in data-driven model predictive control
The development of control methods based on data has seen a surge of interest in recent
years. When applying data-driven controllers in real-world applications, providing theoretical …
years. When applying data-driven controllers in real-world applications, providing theoretical …
Willems' fundamental lemma for state-space systems and its extension to multiple datasets
Willems et al.'s fundamental lemma asserts that all trajectories of a linear system can be
obtained from a single given one, assuming that a persistency of excitation and a …
obtained from a single given one, assuming that a persistency of excitation and a …