MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGY
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098960 - MODEL PREDICTIVE CONTROL
Programma dettagliato e risultati di apprendimento attesi
The course is aimed at providing the main ideas, algorithms, and properties of Model Predictive Control (MPC) and Moving Horizon Estimation (MHE).
The basic formulation of MPC will be introduced for linear systems. Then, the most popular industrial methods will be described, and some application examples will be discussed. Prototype MPC algorithms with guaranteed stability properties will be derived, based on implicit or explicit solutions of the underlying optimization problem to be solved on-line. In the second part of the course some extensions and future research challenges of MPC will be discussed to cope with the problems related to (i) robustness with respect to unmodeled dynamics and/or disturbances, (ii) MPC algorithms for systems subject to stochastic noise, (iii) inclusion of logic constraints, (iv) distributed implementations. Relations of MPC with the problem of constrained state estimation will be highlighted and a prototype Moving Horizon Estimation algorithm will be presented.
The goal is to allow the students to design and implement MPC algorithms in different application scenarios.
1. Motivations
2. Resumé of optimal control and filtering for discrete-time linear systems
3. Introduction to MPC
4. Industrial formulations of MPC
5. Implicit Linear Quadratic optimal control and Receding Horizon implementation
6. Explicit MPC
7. MPC wit stability guarantees
8. Economic MPC
9. Robust MPC
10. Stochastic MPC
11. Hybrid MPC
12. Distributed MPC
13. Moving Horizon Estimation
14. Applications of MPC and sofware environments for the design of MPC controllers
Note Sulla Modalità di valutazione
The final exam will consist in the discussion of the application of MPC algorithms to specific control problems.
Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine
Calendario testuale dell'attività didattica
Bibliografia
E.F. Camacho, C. Bordons, Model predictive control, Editore: Springer, Anno edizione: 2004
J. Maciejowski, Predictive control with constraints, Editore: Prentice Hall, Anno edizione: 2002
J.B. Rawlings, D.Q. Mayne, Model Predictive Control: Theory and Design, Editore: Nob Hill Publishing, Anno edizione: 2009
Software utilizzato
Nessun software richiesto
Mix Forme Didattiche
Tipo Forma Didattica
Ore didattiche
lezione
25.0
esercitazione
0.0
laboratorio informatico
0.0
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
November 16, 2015
Conf. Room (CR) DEIB
14:00-18:00
Nov. 17
Seminar Room (SR) DEIB
14-18
Nov. 18
SR
9-12
Nov. 18
CR
14-18
Nov. 19
SR
10-12
Nov. 19
SR
14-18
Nov. 20
SR
9-13