Study programme 2022-2023 | Français | ||
Optimal Control and Estimation | |||
Programme component of Master's in Electrical Engineering : Specialist Focus on Data Science for Dynamical Systems (MONS) (day schedule) à la Faculty of Engineering |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
UI-M2-IRELBS-002-M | Compulsory UE | VANDE WOUWER Alain | F107 - Systèmes, Estimation, Commande et Optimisation |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Anglais, Français | 32 | 16 | 0 | 0 | 0 | 4 | 4.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-SECO-106 | Optimal Control and Estimation | 32 | 16 | 0 | 0 | 0 | Q1 | 100.00% |
Programme component | ||
---|---|---|
UI-M1-IRELEC-302-M Advanced Control |
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
master the basic concepts of optimal control and filtering;
design linear quadratic regulators (LQR);
investigate more general problems, i.e. nonlinear systems and objective functions expressed in a general form possibly including constraints, that can be solved using Pontryagin Maximum Principle;
use basic dynamic optimization and model predictive control algorithms (for example DMC);
understand the stochastic description of dynamic systems and optimal filtering, Kalman filtering, and its several extensions, as well as receding-horizon observers;
work on various applications, including applications in biomedical and biochemical engineering.
UE Content: description and pedagogical relevance
linear quadratic regulator (discrete and continuous-time versions); Pontryagin maximum principle; basic algorithms of dynamic optimization and model predictive control; observability of nonlinear systems; basic principles of stochastic system representation; Kalman filtering; receding horizon observer; exercises.
Prior Experience
state space equations, controllability, observability, state feedback, observer
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-SECO-106 |
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Mode of delivery
AA | Mode of delivery |
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I-SECO-106 |
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Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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I-SECO-106 | Not applicable |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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I-SECO-106 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
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I-SECO-106 | Arthur Gelb, Applied Optimal Estimation, MIT Press 1974 |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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I-SECO-106 | Authorized |
Term 1 Assessment - type
AA | Type(s) and mode(s) of Q1 assessment |
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I-SECO-106 |
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Term 1 Assessment - comments
AA | Term 1 Assessment - comments |
---|---|
I-SECO-106 | Oral examination relative to exercises (with a written preparation) and theory |
Resit Assessment - Term 1 (B1BA1) - type
AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
---|---|
I-SECO-106 |
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Term 3 Assessment - type
AA | Type(s) and mode(s) of Q3 assessment |
---|---|
I-SECO-106 |
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Term 3 Assessment - comments
AA | Term 3 Assessment - comments |
---|---|
I-SECO-106 | Oral examination relative to exercises (with a written preparation) and theory |