Systems modelling and optimization
Academic Year 2023/2024 - Teacher: CARLO FAMOSOExpected Learning Outcomes
- Knowledge and understanding
Knowledge on modeling of linear systems and optimal and robust control techniques. Nonlinear modeling based on neural algorithms. Linear and nonlinear programming.
- Applied knowledge and understanding
Software tools to solve problems of modeling and optimization.
- Making judgements
The student will be able to autonomously determine the modeling technique more suitable on the basis of the features of the process under consideration. The student will be able to model problems of resource management in terms of liner and nonlinear programming.
- Communication skills
The student will develop the capability of interfacing with process engineers and with non-engineer personnel to model and solve resource management problems.
- Learning skills
The student will be able to discriminate among different programming and optimization problems. The student will be able to select the proper methods for their resolution.
Course Structure
Frontal lectures and Matlab based laboratory.
Required Prerequisites
Basic knowledge on linear systems.
Detailed Course Content
The course aim at providing basic knowledge on modeling and control of linear and nonlinear systems. In particular, optimal and robust control techniques will be discussed. Moreover, neural network based modeling strategies will be presented. Furthermore, linear and nonlinear programming problems will be considered, providing knowledge about the most used algorithms to solve them.
Textbook Information
- 1. L. Fortuna, M. Frasca, A. Buscarino, Optimal and Robust Control - Advanced topics with MATLAB, CRC Press, 2021.
- 2. F. S. Hillier, G.J. Liebermann, Introduction to Operations Research, Ed. McGraw Hill, 11th edition, 2021.
- 3. S. Haykin, Neural Networks and Learning Machines, Pearson, 2016.
| Author | Title | Publisher | Year | ISBN |
|---|---|---|---|---|
| L. Fortuna, M. Frasca, A. Buscarino | Optimal and Robust Control - Advanced topics with MATLAB | CRC Press | 2021 | 9781032053004 |
| F. S. Hillier, G.J. Liebermann | Introduction to Operations Research | Ed. McGraw Hill, 11th edition | 2021 | 9781259872990 |
| S. Haykin | Neural Networks and Learning Machines | Pearson | 2016 | 9789332570313 |
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduzione: Richiami di teoria dei sistemi (Prof. Buscarino) | Testo 1: Cap.1-2 |
| 2 | Concetti fondamentali e terminologia (Prof. Buscarino) | Testo 1: Cap 1-2 |
| 3 | Decomposizione ai valori singolari (Prof. Buscarino) | Testo 1: Cap 4 |
| 4 | Analisi alle componenti principali e Realizzazione bilanciata a catena aperta (Prof. Buscarino) | Testo 1: Cap 5 |
| 5 | Controllo Ottimo (Prof. Buscarino) | Testo 1: Cap 8 |
| 6 | Problemi basati su Linear Matrix Inequalities (Prof. Famoso) | Testo 1: Cap 12 |
| 7 | Metodi di risoluzione di problemi di programmazione lineare (Prof. Famoso) | Testo 2: Cap 4-5 |
| 8 | Metodi di risoluzione di problemi di programmazione lineare (Prof. Famoso) | Testo 2: Cap 4-5 |
| 9 | Metodi di risoluzione di problemi di programmazione binaria e non lineare (Prof. Buscarino) | Testo 2: Cap. 12-13 |
| 10 | Modellistica mediante reti neurali (Prof. Buscarino) | Testo 3 |
Learning Assessment
Learning Assessment Procedures
The exam consists in a test work and an oral examination during which the practical test performed in MATLAB and the theoretical aspects will be discussed.