AA/EE/ME 549: Estimation and System Identification
Spring 2005
MWF 12:30-1:20, Gug. 217

 

Instructor

Prof. Kristi A. Morgansen
morgansen@aa.washington.edu

Office Hours

M 4:30-5:30pm, T 2-3pm
Gug. 310

Teaching Assistant

Michael Frostad
Homework Section:

 


W 4-5pm

 

Course description

Grading

Textbook

Schedule

Course Description

A great many control design and analysis applications involve systems that are not well-understood, and for which detailed models are unavailable. Without an estimate of the state variables of a system, standard control theoretic techniques cannot be applied. To address this problem, system observers have been developed for a number of classes of systems.

This course will focus on development of oberservers and optimal observers for both discrete and continuous time with emphasis on continuous time. Both linear and nonlinear systems will be considered. The course will include a project - with students working in small groups.

The goal of this course is to enable all students to have the skills and knowledge to successfully apply estimation techniques to a variety of applications.

Prerequisites: EE 505, AMATH 506 or STAT 506; Recommended AA/EE/ME 548

Topics:

  1. Dynamical Systems
  2. Least Squares
  3. Probability and Random Variables
  4. Parameter Estimation and System Identification
  5. Kalman Filtering (standard, unscented, scented, extended)
  6. Smoothing
  7. Particle Filters


Grading

  1. Homework: 30%. There will be 8 homework assignments due at 4pm on Wednesdays. Each problem set will have about 6 problems and each problem is worth 5 points. Late homework will not be accepted without prior permission from the instructor.  Homework must be written up following the five steps of problem solving.
  2. Midterm: 20%. The midterm will be a take-home exam posted on April 27 and due on May 4 at 5pm.
  3. Final: 30%. The final exam will be a take-home exam posted on May 27 and due on June 6.
  4. Project: 20%. Students may work individually or in pairs to complete a project of designing an observer for a physical system. For your project, you may choose any system (real or simulated), and build an estimator that reconstructs the system state from observations.  The system may be anything as long as it is a real system (e.g. tracking live fish, tracking robotic fish, estimating the state of polymer chains, tracking vortices in fluid, estimating position and orientation of an aircraft).  Data or simulators for a number of systems can be obtained from various faculty projects.  Project plans are due April 27. The project plan should outline your project according to the five steps of problem solving.  Final project presentations will be during finals week.

 

Homework Section:  There will be an optional weekly homework solving section on Wednesdays from 4-5pm.  In these sections, students will present the solutions to homework problems.  This activity will help with technical presentation skills as well as problem solving techniques.  Students have been assigned two problems to present at this link.  For each problem presented, 5 points of extra credit will be added to the lowest homework score (note that this is the same score for a completed homework problem).  You are welcome to exchange dates/problems with other students, but please let me know when exchanges are made.


Textbook (required)

J. L. Crassidis and J. L. Junkins, "Optimal Estimation of Dynamic Systems," Chapman & Hall/CRC, 2004.

References (on reserve in Engineering Library)

  1. A. Gelb, "Applied Optimal Estimation," MIT Press, 1974.

Schedule

Date

Topics

Reading

Assignments

Mar 28

Introduction

 

 

Mar 30

Linear and Nonlinear Dynamical Systems

3.1-3.6

Homework #1 Assignment

(Solutions)

 

Apr 1

Rigid Body Dynamics

3.7-3.11

 

Apr 4

Linear Least Squares

1.1-1.3

 

Apr 6

Nonlinear Least Squares and Basis Functions

1.4-1.7

Homework #2 Assignment

(Solutions, Matlab files)

Apr 8

Probability

online notes (Gelb 2.2)

 

Apr 11

Random Processes

online notes

 

Apr 13

Minimum Variance

2.1-2.2

Homework #3 Assignment

(Solutions)

Apr 15

Maximum Likelihood

2.3-2.5

 

Apr 18

Bayesian Estimation

2.6-2.8

 

Apr 20

GPS and Attitude Determination

4.1-4.2

Homework #4 Assignment

(Solutions)

Apr 22

Engineering Open House
No class 

 

 

Apr 25

Orbit Determination and Aircraft Parameter Identification

4.3-4.6

 

Apr 27

Discrete Time Kalman Filter

5.1-5.3

Midterm (Solutions)

Apr 29

Continuous Time Kalman Filter

5.4-5.5

 

May 2

Extended Kalman Filter and Colored Noise

5.6

 

May 4

Unscented Kalman Filter

5.7-5.8

Homework #5 Assignment

(Solutions)

May 6

Discrete Fixed Interval Smoothing 

6.1.1 

 

May 9

Continuous Fixed Interval Smoothing

6.1.2

 

May 11

Nonlinear Fixed Interval Smoothing 

6.1.3

Homework #6 Assignment

(Solutions)

May 13

Discrete Fixed Point Smoothing

6.2.1

 

May 16

Continuous Fixed Point Smoothing

6.2.2

 

May 18

Discrete Fixed Lag Smoothing

6.3.1

Homework #7 Assignment

(Solutions)

May 20

Video lecture 1

Video lecture 2

Video lecture 3

Continuous Fixed Lag Smoothing

6.3.2

 

May 23

Histogram Filters

online notes

 

May 25

Particle Filters

online notes

Homework #8 (Solutions)

May 27

Particle Filters

online notes

 

May 30

Memorial Day
Holiday

 

 

Jun 1

Applications and Examples

 

Homework #9 (Solutions)

Jun 3

Applications and Examples

 

 

 

FINAL EXAMINATION