 | Study programme 2021-2022 | Français | |
| Optimal Control and Estimation | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-SECO-106 |
|
|
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais, Français | 32 | 16 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
linear quadratic regulator (discrete and continuous-time versions); Pontryagin maximum principle; dynamic optimization and model predictive control (basic algorithms); observability of nonlinear systems; stochastic system representation; Kalman filtering; receding-horizon observers, exercises.
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Arthur Gelb, Applied Optimal Estimation, MIT Press 1974
Mode of delivery
- Mixed
Type of Teaching Activity/Activities
- Cours magistraux
- Projet sur ordinateur
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term