This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

Control Performance Management in Industrial Automation provides a coherent and self-
contained treatment of a group of methods and applications of burgeoning importance to the …

We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …

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 …

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 …

This book categorizes the wide area of data-driven model-free controllers, reveals the exact
benefits of such controllers, gives the in-depth theory and mathematical proofs behind them …

A typical design procedure for model predictive control or control performance monitoring
consists of: identification of a parametric or nonparametric model; derivation of the output …

This paper deals with the data-driven design of observer-based fault detection and control
systems. We first introduce the definitions of the data-driven forms of kernel and image …

This paper shows the design of predictive controllers using the predictor, designed from the
subspace matrices, obtained directly from the input/output data. The model-free design …

In this paper, a closed-loop subspace identification approach through an orthogonal
projection and subsequent singular value decomposition is proposed. As a by-product of …