M. Lovera  
  

 
 
 
 
 
estimation in aerospace

Welcome to the home page of the Estimation in Aerospace course for the Aerospace Engineering programme.

The course aims at providing an introduction to estimation theory, with specific reference to the methods and tools which are relevant to present-day aerospace applications, such as parameter estimation, model identification, state estimation for navigation, control and fault detection purposes. For each of the considered topics some theoretical background is first provided, followed by its specialisation to the case of linear (nonlinear when possible) dynamic models and one or more examples of application to aerospace engineering problems. The final part of the course is eventually devoted to the presentation of a few case studies.

The course is organised in one module of 8 cfu and will start on september 18 2017.

Presentation of the course

 
 
  
 
COURSE SCHEDULE 2017/18
 
Day Time Room
Monday
14.30-16.15
BL27.16
Tuesday
   9.30-11.15
L0.4
Thursday
   9.30-11.15
L0.2
Thursday 16.30-18.15
BL27.15
     
EXAM SCHEDULE
     
Day Room
     
     
     
     
     
     

 

  
    course program    
   

Part 1: introduction to estimation in aerospace

  1. Overview of estimation problems in aerospace: sensor calibration, parameter estimation, model identification, state estimation, navigation, fault detection, fault tolerant control.
  2. Recap of probability and statistics
  3. Introduction to the theory of estimation.
  4. Introduction to model identification: problem statement; grey vs black box models; linear vs nonlinear models; the notions of structural and experimental identifiability.
  5. The model identification process: from experiment design to model validation.

Part 2: parametric and non-parametric estimation

  1. Estimation theory: the maximum likelihood method; least squares estimation.
  2. Time-domain output error identification of nonlinear state space models. 
  3. Experiment design
  4. Time-domain and frequency-domain output error identification of linear state space models.
  5. Random processes and linear systems
  6. Spectral estimation and frequency response function estimation

Part 3: state estimation and equation error model identification

  1. Introduction to the state estimation problem.
  2. Estimation theory: introduction to Bayesian estimation.
  3. Optimal state estimation for linear systems: the Kalman filter.
  4. Time-domain equation error identification of linear state space models.
  5. State estimation for nonlinear systems: the Extended Kalman filter; overview of more general estimation schemes.
  6. Kalman filters: implementation issues.
  7. Attitude determination: the multiplicative extended Kalman filter.

Part 4: black-box linear model identification

  1. Problem statement: structure selection vs parameter estimation.
  2. Time- and frequency-domain identification of SISO linear models.
  3. Identification of MIMO linear models: introduction to subspace methods

Part 5: case studies

  1. Identification of control-oriented models for helicopter flight mechanics.
  2. Identification of the attitude dynamics for a variable-pitch quadrotor UAV.
  3. Model-based control law design for small-scale and full-scale rotorcraft.

 

      
           
    references and material      
   

References:

  • Material to be provided online here (in preparation).
  • Recommended reading:
    • Vladislav Klein and Eugene A. Morelli, Aircraft System Identification: Theory And Practice, AIAA, 2006.
    • Mark Tischler and Robert Remple, Aircraft and Rotorcraft System Identification, AIAA, 2006.
    • Alan Stuart, Keith Ord, Steven Arnold, Kendall's Advanced Theory of Statistics, Classical Inference and the Linear Model (Volume 2A), Wiley, 2010.
    • Allan G. Piersol and Julius S. Bendat, Engineering Applications of Correlation and Spectral Analysis, Wiley, 1993.
    • Sergio Bittanti, Teoria della predizione e del filtraggio, Pitagora Editrice, 2002.

Material for part 1: introduction to estimation in aerospace

  1. Overview of estimation problems in aerospace: sensor calibration, parameter estimation, model identification, state estimation, navigation, fault detection, fault tolerant control.
  2. Recap of probability and statistics.
  3. Introduction to the theory of estimation.
  4. Introduction to model identification: problem statement; grey vs black box models; linear vs nonlinear models; the notions of structural and experimental identifiability.
  5. The model identification process: from experiment design to model validation.

Material for part 2: parametric and non-parametric estimation

  1. Estimation theory: the maximum likelihood method; least squares estimation.
  2. Time-domain output error identification of nonlinear state space models. 
  3. Time-domain and frequency-domain output error identification of linear state space models.
  4. Experiment design.
  5. Random processes and linear systems.
  6. Spectral estimation and frequency response function estimation.

Material for part 3: state estimation and equation error model identification

  1. Introduction to the state estimation problem.
  2. Estimation theory: introduction to Bayesian estimation.
  3. Optimal state estimation for linear systems: the Kalman filter.
  4. Time-domain equation error identification of linear state space models.
  5. State estimation for nonlinear systems: the Extended Kalman filter; overview of more general estimation schemes.
  6. Kalman filters: implementation issues.
  7. Attitude determination: the multiplicative extended Kalman filter.

Material for part 4: black-box linear model identification

  1. Problem statement: structure selection vs parameter estimation.
  2. Time- and frequency-domain identification of SISO linear models.
  3. Identification of MIMO linear models: introduction to subspace methods.

Material for part 5: case studies

  1. Identification of control-oriented models for helicopter flight mechanics.
  2. Identification of the attitude dynamics for a variable-pitch quadrotor UAV.
  3. Model-based control law design for small-scale and full-scale rotorcraft.

 

      
         
    exam      
   

 

      
    contacts      
   
  • Marco Lovera

Dipartimento di Scienze e Tecnologie Aerospaziali

Politecnico di Milano

Tel. +39-02-23993592

email: marco.lovera@polimi.it