The Automatica research group at DET-PoliTo is involved in both fundamental research and applications of methods and technologies for the analysis, identification and control of dynamical systems; efficient modeling and optimization for large-scale complex and networked systems, machine learning, data analytics, convex optimization, and robotics.
The main theoretical research activities of the Automatica group include model-based control design for linear and nonlinear systems; Model Predictive Control; data-driven control design; 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; direct virtual sensor design for linear and nonlinear systems; sparse optimization for data analytics; Machine Learning; financial modelling and investment decision support; single-period and multi-period portfolio allocation.
Applied research is performed in different fields, including computational finance, energy (power grid analysis, energy efficiency in buildings, high altitude wind energy generation using power kites, demand prediction), environment (water and atmospheric pollution forecast), aerospace (power kites for naval propulsion and wind energy generation, spacecraft control, quadrotor control), automotive (control of suspension systems and vehicle lateral dynamics, engine control), biomedical engineering (identification and control of diabetes systems, cell population control), 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).
We are open for consulting and/or for collaboration with industrial and professional partners on regional, national and international research projects.
Research focus groups
The research personnel and activities and of the Automatica research group are organized into four main sub-areas:
- Control systems and automation
- Optimization and data science
- Industrial, mobile, and service robotics
- Networked systems, opinion dynamics and consensus
- Advanced nonlinear model predictive control: Theory, numerical aspects, and applications
- Advanced robotic solutions for innovative manufacturing systems
- Analysis and control of hybrid systems
- Augmented neural networks
- Collision detection for industrial manipulators
- Combined grey-black-box (GBB) model identification.
- Control of mobility-on-demand systems
- Data-driven control design for nonlinear systems
- Data-driven control of autonomous vehicle and fleets
- Data-driven control of biomedical systems
- Design of advanced control algorithms for aerospace applications
- Design of experiments
- Design of observers for nonlinear systems
- Development and assessment of model-based and sensor-based algorithms for combustion and emission control in diesel engines
- Direct virtual sensors
- Friction modelling and identification for industrial manipulators
- Game theory in dynamical games
- Innovative robotic solutions for planetary exploration missions
- Inversion of nonlinear maps
- Modeling and Control of Mobility-on-Demand Systems
- Modeling, optimization, and control of complex and networked systems
- Nonlinear model predictive control for automotive applications
- Polynomial optimization in control
- Pose graph estimation in robotics
- Quantum optimization for complex system design and control
- Stochastic systems
- Study of GNSS technologies, services, and applications
- System identification via machine learning
- Text data analytics
- Unmanned aerial vehicles for smart cities
- Unsupervised real-time seed detection for precision agriculture seeding machine