**MAIN RESEARCH ACTIVITIES:**

**Direct data-driven control system design
**

For many industrial applications, finding a model from physical laws that is both simple and reliable for control
design is a tough undertaking. When a set of measurements is available, the control law can be computed
from data without relying on knowledge of the underlying physics. Specifically, in “indirect” data-driven
approaches, a model of the system is first derived from data and then a controller is computed based on such
a model. In “direct” data-driven approaches, the controller is directly derived from experimental data, such
that process dynamics are automatically considered relevant or not, depending only on their weight on the
final control index. The main advantages of such techniques are that they are insensitive to modeling errors
and less time-consuming.

The first aim of this research work is to develop mathematical tools so as to extend existing data-driven
methods to a larger class of industrially relevant problems. These methodological extensions include, among the others,
direct data-driven design for multivariable plants (in collaboration with JKU), direct LPV control (in collaboration with TU Eindhoven) and direct data-driven feed-forward linearization (in collaboration with TU Delft).

Furthermore, since it is common belief that finding a good model of the plant is always the best way towards
controller design, a secondary goal of this activity is to provide a quantitative
assessment of direct data-driven
techniques and show whether - and in which cases - they might be preferable.

Finally, since it can be proven that the weak point of direct data-driven methods is their statistical performance,
a third aim of this activity is to find mathematical solutions to __improve the overall efficiency__ of the
controller estimate. From this perspective, two directions are addressed, namely optimal experiment design and L2 regularization (both in collaboration with EPFL).

**Automotive control
**

Nowadays, vehicle systems are definitely among the most challenging platforms for research in automatic control.
Almost all categories of vehicles are now equipped with sophisticated sensors and
electronic control units able to process the available information on engine and vehicle dynamics. It follows
that this information can be exploited, e.g., to increase the level of safety, decrease the
fuel consumption, deal with environmental constraints. Moreover, “smart vehicles” can be used to communicate
among each other towards the establishment of “smart cities” with sustainable transports and optimized
traffic flows. In this interesting field, the research activity has been focused, among the others, on:
vehicle dynamics dynamics, Diesel engine control, optimal energy management policies (see the SHE simulator webpage).