Control Engineering

Danilo Menegatti
Danilo Menegatti

General objectives

The course aims at applying advanced dynamic control methodologies to networks/systems by adopting a technologically independent abstract approach that addresses the problem of network/system control, leaving aside specific network/system technologies. Students will be able to design control actions suitable for communication, energy, transport, security and health networks/systems


Specific objectives

Knowledge and understanding:
Students will be able to understand the specificity of some application environments such as those of communication, energy, transport, security and health networks/systems, as well as to abstractly model and control these networks/systems. Furthermore, if the modeling of such networks/systems is impossible or too complex to implement, students will be able to use data-driven techniques capable of combining control methodologies with artificial intelligence/machine learning methodologies.

Apply knowledge and understanding:
Students will be aware of the main problems and able to design control actions applicable to communication, energy, transport, safety and health networks/systems aimed at satisfying assigned design specifications..

Critical and judgment skills:
Students will be able to choose the most suitable control methodologies for specific problems and to evaluate the complexity of the proposed solutions.

Communication skills:
The course activities allow the students to be able to communicate/share (i) the main problems relating to communication, energy, transport, safety, health networks/systems, (ii) possible design choices for the control of such networks/systems . Furthermore, the course includes the possibility of carrying out application theses on topics related to projects carried out by the research group coordinated by the teacher; as part of these activities, students will acquire the ability to collaborate in groups.

Learning ability:
The course development methods aim to create a mindset of the student oriented to the control of complex systems/networks, by appropriately combining methodologies coming from the automation field and from various other engineering areas.

Students will be able to design control actions for communication networks, energy distribution networks and e-health systems; they will also be aware of the main issues related to security.

No pre-requirement is necessary.
There are no pre-requisit exams.

The first part of the course (about 40 hours) details the following methods: Markov Decision Process, Dynamic Programming, Reinforcement Learning (in particular, TD learning, Sarsa, Q-learning), Machine Learning (k-means clustering, clustering). Besides the theoretical aspect, the course considers the practical use of such methods for the control of communication, transport, security, health systems/networks.
The second part of the course (about 20 hours, carried out in parallel with the first part), held in constant synergy with the research projects funded by the European Union, (i) provides an overview of up-to-date control problems related to communication, energy, transport, security and health, (ii) details how the control methods considered in the first part of the course, as well as other control methods introduced in previous courses (e.g. Model Predictive Control) can be used to solve the above-mentioned problems.

- R.S. Sutton and A.G. Barto, "Reinforcement Learning: An Introduction," MIT Press, 1998
- Cristopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
- John D. Kelleher, "Deep Learning", MIT Press, 2019.
- Lecture notes based on the seminar materials.

Traditional with possible project.
The course is taught by using the blackboard and/or slides depending on the topic.

Attendance at the lessons is not compulsory, but it is strongly recommended.

Written exam with oral discussion or thesis ("tesina") evaluation.
The written test consists of two questions on the theoretical part of the course and a very general question on one of the seminars held. Indicatively, each question weighs for one third of the evaluation. All questions are open-ended.
Alternatively, the student can opt for an in-depth essay (with personal contribution required), to bedone at home, of one of the seminars held during the course, upon approval.

Project on multi-agent control of a cellular system, or a smart grid, or a health system, or a critical infrastructure.