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Scheda Riassuntiva
Anno Accademico 2019/2020
Tipo incarico Dottorato
Insegnamento 055049 - MODEL PREDICTIVE CONTROL
Docente Farina Marcello
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGYAZZZZ055049 - 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 07/09 (14:00-18:00):

- Motivations
- Introduction to MPC
- Formulation and properties
 
Tuesday 08/09 (09:00-12:00):
- Industrial formulations
- Robust MPC

Tuesday 08/09 (14:00-17:00): Stochastic MPC
 
Wednesday 09/09 (09:00-12:00): Explicit/approximated MPC
 
Wednesday 09/09 (14:00-17:00):
- Hybrid MPC
- Economic MPC
- Introduction to distributed MPC
 
Thursday 10/09 (09:00-12:00):
- Distributed MPC forumations
- Moving Horizon Estimation
  
Thursday 10/09 (14:00-18:00): Tutorial session

Friday 11/09 (09:00-11:00): Case studies and applications
 


Bibliografia

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
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese

Note Docente
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
28/01/2020