Neural Networks for Optimal Control
ME-717 / 2 crédits
Enseignant(s): Ferrari Trecate Giancarlo, Massai Leonardo, Saccani Danilo
Langue: Anglais
Frequency
Only this year
Summary
The effectiveness of control algorithms in large-scale cyber-physical systems relies not only on advancements in sensing, computation, and communication but also on the availability of methods to design controllers capable of stabilizing nonlinear systems under nominal operating conditions.
Content
1. Designing Optimal Closed-Loop Maps for Linear Systems
- Stable transfer matrices, internal stability, Youla parametrization, Internal Model Control (IMC)
- Convex optimal control over all stabilizing policies: guarantees for both model-based and model-free cases
- Finite-dimensional approximations and state-space implementations
2. Performance Boosting for Nonlinear Optimal Control
- Signal-space notation, nonlinear stable operators, L2 gains, and the small-gain theorem
- IMC parametrization of stabilizing nonlinear policies, robustness for uncertain models
- NN parametrizations of stabilizing controllers
3. Performance Boosting at Scale
- Dissipativity for interconnected systems Distributed Performance Boosting
Note
The course will be offered in partnership with the International Graduate School on Control (IGSC) of the European Embedded Control Institute (EECI) and, therefore, will be open to external students (see http://www.eeci-igsc.eu/).
Dans les plans d'études
- Matière examinée: Neural Networks for Optimal Control
- Cours: 18 Heure(s)
- Exercices: 4 Heure(s)
- Type: obligatoire