This area involves both fundamental research and applications, concerned with analysis, identification, optimization and control of complex dynamic systems.

Fundamental research includes model-based control design; data-driven control design; Model Predictive Control; scenario methods for probabilistic robust control; modeling and control for industrial and service robotics; modeling, optimization, and control of large-scale and networked systems; model identification and prediction; observer/virtual sensor design; sparse optimization for data analytics; Machine Learning; financial modelling and investment decision support; single-period and multi-period portfolio allocation; multivariate polynomial optimization.

Applied research includes computational finance, energy (power grid analysis, energy efficiency in buildings, demand prediction), environment (water and atmospheric pollution forecast), aerospace systems (spacecraft control, quadrotor control), automotive systems (autonomous driving, control of suspension systems and vehicle lateral dynamics, engine control, virtual sensors), biomedical engineering (identification and control of diabetes systems), sustainable mobility (modeling and optimization of shared vehicle systems), multi-agent systems (coordination and cooperation), socio-technical systems (epidemic spreading, diffusion of innovation), industrial and mobile robotics (motion planning and control, collision avoidance and detection, friction modelling and compensation, monitoring and programming of robotic cells, UAVs planning and control, cooperative robotics), food and agriculture (modeling, control, and robotics applications in the food processing industry, and in precision farming and agriculture).