Direct data-driven control
The first aim of this research work is to develop model-free control design methods. Such techniques include, among the others, direct data-driven design for multivariable plants (in collaboration with JKU), direct LPV control (in collaboration with TU Eindhoven), direct data-driven feed-forward linearization (in collaboration with TU Delft) and optimal reference model selection (see here). 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 (in collaboration with EPFL).
Recent developments and online documents:

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).