Control Systems

Director: Alberto Bemporad

Curriculum overview

The curriculum in Control Systems is oriented towards model-based control of dynamical systems and decision-making algorithms, including embedded optimization algorithms for control and management of stochastic, networked, and large-scale dynamical systems. Motivated by the pervasive nature of data information systems and by the availability of powerful (and possibly distributed) computational resources, the main goal is to devise complex decision-making strategies that make systems react with a certain degree of autonomy and in an intelligent way to changes in their operating environment. Research skills in model-based control and optimization of dynamical systems taught enable students to conceive novel theories and algorithms. Students also learn professional skills for designing, simulating, and deploying control systems in a variety of application areas, such as smart grids and energy markets, finance, automotive and aerospace systems, water network management, industrial processes, and many others.

Input and Output Profiles

This curriculum aims at preparing researchers and professionals with a wide knowledge of the theoretical foundations of Control Systems and Optimization, that are able to analyze and propose constructive solutions to a large variety of real-life problems of industrial, managerial, economic, and societal interest. Perspective students should preferably have a master-level background in computer science, engineering, physics, mathematics, management science, statistics or in a related field. Graduates from the curriculum are qualified to work in universities, public and industrial research centers, and to take on professional roles and high-profile tasks and responsibilities in both private companies and public institutions.

Reference area(s): Systems Engineering (main), Computer Science, Economics and Management.

Research Units contributing to the curriculum: DYSCO - Dynamical Systems, Control, and Optimization (main), MUSAM - Multi-scale Analysis of Materials, PRIAn - Pattern Recognition and Image Analysis, Networks - Complex Networks, LIME - Laboratory of Innovation Management and Economics.

PhD candidates also have the opportunity to collaborate with other institutions that work with IMT Research Units.

Coursework: See full course list