Hours:
24 hours (6 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

The availability of an unprecedented level of computational power and data – both real and synthetic - opens a whole new range of possibilities in several fields. This is strongly impacting the way to plan and control the behavior of systems. The mainstream approach apply supervised/unsupervised machine learning algorithms for which it is hard to provide formal certificates on important properties like convergence and stability.
This course presents an alternative approach able to provide such guarantees leveraging upon a minimalistic model knowledge: the iterative learning control.
First the theoretical foundations of the method will be covered for linear and nonlinear discrete and continuous systems, hence an overview of recent applications pertaining to the robotic filed will be given. Finally theoretical open problems will be discussed.

Course Contents in brief:

1. Introduction (0.5 h)
2. Iterative Learning Control (ILC) for linear systems (3 h)
3. ILC for nonlinear systems (3.5 h)
4. From input-state to input-output (3.5 h)
5. Feed-forward and Feedback ILC (3.5 h)
6. Matlab Implementation (3.5 h)
7. Examples (3.5 h)
8. Open Problems in ILC Theory and Applications (3.5 h)

Schedule:

Monday 29, July – Friday 2 August 2024