The University of Newcastle, Australia
Available in 2019

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.


Availability2019 Course Timetables

Callaghan

  • Semester 1 - 2019

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

ENGG6440Linear Control and EstimationThis 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.FENBEFaculty of Engineering and Built Environment512School of Engineering1060005940Semester 1 - 2019CALLAGHANCallaghan2019ENGG2440 Modelling and Control1. Review of relevant concepts2. State feedback, pole placement, controllability3. LQ control4. Linear observers, observability5. Gaussian noise and density6. Minimum mean squared estimation with special cases (LS, WLS, BLUE, MAP, Kalman filter)7. Separation principle and LQG8. MPC for linear systems YOn successful completion of this course, students will be able to:1Obtain 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 control2Design and implement advanced controllers3Demonstrate an advanced understanding of methods used to model noise, and use these model in advanced control design4Demonstrate expertise in linear estimation methods including minimum squared estimation and Kalman filters This course has similarities to ELEC4410 and ENGG3440. If you have successfully completed ELEC4410 or ENGG3440 you cannot enrol in this course.Formal Examination: Final ExaminationQuiz: Quizzes x 2Tutorial / Laboratory Exercises: Laboratory Exercises x 6 CallaghanLaboratoryFace to Face On Campus2hour(s)per Week for0Full Term3LectureFace to Face On Campus3hour(s)per Week for0Full Term1TutorialFace to Face On Campus1hour(s)per Week for0Full Term2


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 starting in week 3

Lecture

Face to Face On Campus 3 hour(s) per Week for Full Term starting in week 1

Tutorial

Face to Face On Campus 1 hour(s) per Week for Full Term starting in week 2