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Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2019/2020
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 097495 - ESTIMATION IN AEROSPACE
Docente Lovera Marco
Cfu 8.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - BV (469) AERONAUTICAL ENGINEERING - INGEGNERIA AERONAUTICA*AZZZZ097495 - ESTIMATION IN AEROSPACE
Ing Ind - Inf (Mag.)(ord. 270) - BV (470) SPACE ENGINEERING - INGEGNERIA SPAZIALE*AZZZZ097495 - ESTIMATION IN AEROSPACE

Obiettivi dell'insegnamento

The goal of the course is to enable students to master the engineering methods and tools necessary to formulate and solve model identification problems and to design and implement state estimation systems, with specific reference to Aerospace Engineering.

 


Risultati di apprendimento attesi

Lectures and exercise sessions will allow students to:

  • Acquire a more advanced knowledge of probability, statistics and estimation theory.
  • Formulate and solve data analysis and model identification problems.
  • Design and implement state estimators for, e.g., aircraft and spacecraft attitude determination and navigation.

Argomenti trattati

Part 1: introduction to estimation in aerospace.

- Overview of estimation problems in aerospace: sensor calibration, parameter estimation, model identification, state estimation, navigation, fault detection, fault tolerant control.

- Introduction to the theory of estimation

- Introduction to model identification: problem statement; grey vs black box models; linear vs nonlinear models; the notions of structural and experimental identifiability.

- The model identification process: from experiment design to model validation.


Part 2: parameter estimation and output error model identification

- Estimation theory: the maximum likelihood method; least squares estimation.

- Time-domain output error identification of linear state space models.

- Frequency-domain output error identification of linear state space models.

- Time-domain output error identification of nonlinear state space models.


Part 3: state estimation and equation error model identification

- Estimation theory: introduction to Bayesian estimation.

- Optimal state estimation for linear systems: the Kalman filter.

- Time-domain equation error identification of linear state space models.

- State estimation for nonlinear systems: the Extended Kalman filter; overview of more general estimation schemes.

- Kalman filters: implementation issues.


Part 4: black-box linear model identification

- Problem statement: structure selection vs parameter estimation.

- Time- and frequency-domain identification of SISO linear models.

- Identification of MIMO linear models: introduction to subspace methods.


Part 5: case studies

- Identification of control-oriented models for helicopter flight mechanics.

- Attitude determination for a quadrotor UAV.

- Model-based control law design for small-scale and full-scale rotorcraft.


Prerequisiti

Students are required to know the basic principles of probability and statistics and of linear systems theory, both for continuous-time and discrete-time systems.


Modalità di valutazione

The students will be able to choose between an oral exam and one of the desidata analysis, model identification and state estimation  projects which will be presented during the course.

In both cases the evaluation will be also based on the clarity of the presentation.

During the exam the students shall demonstrate that:

  • they have acquired a working knowledge of probability and statistics.
  • they understand maximum likelihood and Bayesian estimation theory.
  • they are able to formulate and solve data analysis and model identification problems.
  • they can design and implement state estimators for, e.g., aircraft and spacecraft attitude determination and navigation.

Bibliografia
Risorsa bibliografica obbligatoriaOnline course material http://www.aero.polimi.it/lovera
Note:

(in preparation)

Risorsa bibliografica facoltativaSergio Bittanti, Model Identification and Data Analysis, Editore: John Wiley & Sons, Inc, Anno edizione: 2019, ISBN: 9781119546405
Risorsa bibliografica facoltativaVladislav Klein and Eugene A. Morelli, Aircraft System Identification: Theory And Practice, Editore: AIAA, Anno edizione: 2006
Note:

978-1563478321

Risorsa bibliografica facoltativaMark Tischler and Robert Remple, Aircraft and Rotorcraft System Identification, Editore: AIAA, Anno edizione: 2006, ISBN: 978-1563478376
Risorsa bibliografica facoltativaAlan Stuart, Keith Ord, Steven Arnold, Kendall's Advanced Theory of Statistics, Classical Inference and the Linear Model (Volume 2A), Editore: Wiley, Anno edizione: 2010, ISBN: 978-0470689240

Software utilizzato
Nessun software richiesto

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
52:00
78:00
Esercitazione
28:00
42:00
Laboratorio Informatico
0:00
0:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 80:00 120:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.9 / 1.6.9
Area Servizi ICT
30/11/2021