Available in 2018

Course handbook

Description

This course provides students with an in-depth coverage of optimisation-based state feedback control concepts as well as the linear observers needed to design such controllers. Topics covered include Kalman filters, Linear Quadratic Gaussian Control, and Model Predictive Control. In particular, the minimum mean squared error estimation is used to derive Kalman filters for optimal state estimation.


Availability2018 Course Timetables

Callaghan

  • Semester 1 - 2018

Learning outcomes

On successful completion of the course students will be able to:

1. Obtain expertise in state feedback based control system design; appreciate underlying advantages and disadvantages of some advanced control design methods such as linear quadratic Gaussian control and model predictive control

2. Design and implement advanced controllers

3. Demonstrate an advanced understanding of methods used to model noise, and use these model in advanced control design

4. Demonstrate expertise in linear estimation methods including minimum squared estimation and Kalman filters


Content

1. Review of relevant concepts

2. State feedback, pole placement, controllability

3. LQ control

4. Linear observers, observability

5. Gaussian noise and density

6. Minimum mean squared estimation with special cases (LS, WLS, BLUE, MAP, Kalman filter)

7. Separation principle and LQG

8. MPC for linear systems


Requisite

This course has similarities to ELEC4410 and ENGG3440. If you have successfully completed ELEC4410 or ENGG3440 you cannot enrol in this course.


Assumed knowledge

ENGG2440 Modelling and Control


Assessment items

Formal Examination: Final Examination

Quiz: Quizzes x 2

Tutorial / Laboratory Exercises: Laboratory Exercises x 6


Contact hours

Callaghan

Laboratory

Face to Face On Campus 2 hour(s) per Week for Full Term

Lecture

Face to Face On Campus 3 hour(s) per Week for Full Term

Tutorial

Face to Face On Campus 1 hour(s) per Week for Full Term