Fundamentals of Model Predictive Control (Ph.D. course)
This Ph.D. course aims to provide the fundamental concepts of the Model Predictive Control theory. In particular, the course will focus on MPC stability, robustness, tracking MPC, and economic MPC.
Lesson 1: Preliminaries
Lesson 2: Stability
Lesson 3: Tracking MPC
Lesson 4: Economic MPC
Teaching material can be found here.
Control and Modeling of Biological Systems (6 CFU)
The Control and Modeling of Biological Systems (175003) course is part of the master’s carrier in Medical Engineering. The aim of the course is to provide:
1. knowledge of dynamical systems, their property, and how to estimate them using experimental data.
2. knowledge of compartmental dynamical systems typically used in biology, pharmacology, and physiology.
3. basic knowledge of control techniques for such kinds of dynamical systems.
Advanced Multivariable Control (6 CFU)
The Advanced Multivariable Control course (21066) is intended for students enrolled in the Computer Engineering program.
The goal of the course is to provide students with advanced knowledge and skills in methodologies for the analysis of multivariable dynamic systems and the design of advanced controllers.
By the end of the course, students will be able to:
– Study the stability of dynamic systems using Lyapunov’s criterion
– Analyze the structural properties of dynamic systems
– Design multivariable control systems
– Design a Linear Quadratic Regulator (LQR)
– Design a Model Predictive Controller (MPC)
Data Analysis Lab (3 CFU)
The Data Analysis Lab (148021-1) is part of both the Technological Lab and the Management Lab in the Master Degree in Medical Engineering.
The aim of the course is to train the student to work in team to address an assigned project. The practical activities of the laboratory are aimed at improving technical and communication skills.
The lab activities will concern on practical activities focused on learning static and dynamic systems. The explored techniques will mainly be linear and logistic regression for static systems and PEM techniques for dynamic systems identification.