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Scheda Riassuntiva
Anno Accademico 2023/2024
Tipo incarico Dottorato
Insegnamento 061663 - MODEL PREDICTIVE CONTROL
Cfu 5.00 Tipo insegnamento Monodisciplinare
Docenti: Titolare (Co-titolari) Farina Marcello (Scattolini Riccardo, La Bella Alessio, Fagiano Lorenzo Mario)

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGYAZZZZ061663 - MODEL PREDICTIVE CONTROL

Programma dettagliato e risultati di apprendimento attesi

MISSION AND GOALS

The course is aimed at providing the main ideas, algorithms, and properties of Model Predictive Control (MPC) and Moving Horizon Estimation (MHE). MPC, in particular, is the most widely used and successful advanced control method in the process industry and is nowadays also applied in distribution networks, coordination of autonomous systems, automotive, and in many other fields of application.

Lectures and computer sessions will allow students to:
· Understand the challenges, opportunities, and issues related to MPC algorithms.
· Understand the main common technical tools used in the analysis and design of basic and advanced MPC algorithms.
· Learn how to design and practically implement standard and advanced MPC-based algorithms.

PROGRAMME OF THE COURSE

In this course the MPC basic problem formulation is first introduced, along with its main properties. Also, the tools used for guaranteeing theoretical properties (i.e., recursive feasibility and convergence) are discussed in a simplified setting. Then, the most popular industrial MPC formulations will be described.

Advanced problem formulations will be described in details, i.e.,
- robust and stochastic MPC formulations, with special focus on analytic and scenario-based methods;
- explicit MPC and its approximated implementations;
- hybrid MPC, to include logic constraints and integer decision variables;
- economic MPC, that guarantees plant-wide optimality;
- distributed and decentralized MPC implementations.

Constrained optimization-based state estimation will be also considered and a prototype Moving Horizon Estimation algorithm will be presented.

An overview of successful application case studies of different MPC-based algorithms will be presented.

Finally, the main computational tools for MPC implementation in both the linear and nonlinear settings will be presented. A dedicated computer session will be devoted to implementation details, possibly considering selected case studies.


Note Sulla Modalità di valutazione

The exam aims to evaluate, not only the students’ knowledge and understanding of the course topics, but also their ability to apply the acquired concepts and theoretical/practical tools. In particular, a project work consisting of the design of an MPC algorithm for a control problem chosen by the student must be completed and presented in a short oral discussion.


Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica

Monday 02/09 (14:00-18:00): Motivations, introduction to MPC, formulations and properties.

Tuesday 03/09 (9:00-12:00 & 14:00-17:00): Industrial formulations, robust MPC, stochastic MPC, scenario MPC.

Wednesday 04/09 (9:00-12:00 & 14:00-17:00): explicit/approximated MPC, hybrid MPC, economic MPC, introduction to distributed MPC.

Thursday 05/09 (9:00-12:00): distributed MPC.

Friday 06/09 (9:00-12:00 & 14:00-18:00): moving horizon estimation, case studies and applications, tutorial session.


Bibliografia

Software utilizzato
Nessun software richiesto

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
22.0
esercitazione
0.0
laboratorio informatico
4.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese

Note Docente
Slides will be made available by the teachers on the webeep portal, where a complete reference list is provided. The course is residential, in presence.
schedaincarico v. 1.10.0 / 1.10.0
Area Servizi ICT
23/07/2024