From data and structure to models and controllers

Systems and control theory deals with analyzing dynamical systems and shaping their behavior by means of control. Dynamical systems are widespread, and control theory therefore has numerous applications ranging from the control of aircraft and spacecraft to chemical process control. During the last decades, a series of remarkable new control techniques have been developed. The majority of these techniques rely on mathematical models of the to-be-controlled system. However, the growing complexity of modern engineering systems complicates mathematical modeling. In this thesis, we therefore propose new methods to analyze and control dynamical systems without relying on a given system model. Models are thereby replaced by two other ingredients, namely measured data and system structure. In the first part of the thesis, we consider the problem of data-driven control. This problem involves the development of controllers for a dynamical system, purely on the basis of data. We consider both stabilizing controllers, and controllers that minimize a given cost function. Secondly, we focus on networked systems. A networked system is a collection of interconnected dynamical subsystems. For this type of systems, our aim is to reconstruct the interactions between subsystems on the basis of data. Finally, we consider the problem of assessing controllability of a dynamical system using its structure. We provide conditions under which this is possible for a general class of structured systems

Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control

This paper presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient

Cyclic AMP, folic acid and pterin-mediated protein carboxymethylation in cellular slime molds

AbstractIn aggregative cells of Dictyostelium discoideum, extracellular cAMP induces transient methylation of a Mr 46 000 protein. Starvation induces a 10–100-fold increase in the number of cAMP-receptors, but no change in the amount of the methyl accepting protein. In vegetative amoebae, a temporal increase of methylation of the protein is induced by stimulation with folic acid. Aggregative amoebae of Dictyostelium lacteum also contain a Mr 46 000 protein, which is methylated after addition of the attractant monapterin. Therefore, protein carboxymethylation seems to be a general phenomenon during chemotaxis of the cellular slime molds

A Matrix Finsler’s Lemma with Applications to Data-Driven Control

In a recent paper it was shown how a matrix S-lemma can be applied to construct controllers from noisy data. The current paper complements these results by proving a matrix version of the classical Finsler's lemma. This matrix Finsler's lemma provides a tractable condition under which all matrix solutions to a quadratic equality also satisfy a quadratic inequality. We will apply this result to bridge known data- driven control design techniques for both exact and noisy data, thereby revealing a more general theory. The result is also applied to data-driven control of Lur'e systems