Systems modelling and optimization
Academic Year 2022/2023 - Teacher: Arturo BUSCARINOExpected Learning Outcomes
Knowledge on modeling of linear systems and optimal and robust control techniques. Nonlinear modeling based on neural algorithms. Linear and nonlinear programming.
Course Structure
Frontal lectures and Matlab based laboratory.
Required Prerequisites
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.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Introduzione: Richiami di teoria dei sistemi | Testo 1: Cap.1-2 |
| 2 | Concetti fondamentali e terminologia | Testo 1: Cap 1-2 |
| 3 | Decomposizione ai valori singolari | Testo 1: Cap 4 |
| 4 | Analisi alle componenti principali e Realizzazione bilanciata a catena aperta | Testo 1: Cap 5 |
| 5 | Sistemi simmetrici | Testo 1: Cap 7 |
| 6 | Controllo Ottimo | Testo 1: Cap 8 |
| 7 | Problemi basati su Linear Matrix Inequalities | Testo 1: Cap 12 |
| 8 | Introduzione alla programmazione Lineare | Testo 2: Cap. 1-2-3 |
| 9 | Metodi di risoluzione di problemi di programmazione lineare | Testo 2: Cap 4-5 |
| 10 | Metodi di risoluzione di problemi di programmazione binaria e non lineare | Testo 2: Cap. 12-13 |
| 11 | Modellistica mediante reti neurali | Testo 3 |