This course introduces students to optimisation-based state feedback control concepts as well as the linear observers needed to design such controllers. It also covers linear quadratic control design, and introduces the noise models, principles of minimum mean squared error estimation, and Kalman filters for optimal state estimation. These concepts are used to introduce linear quadratic Gaussian control and model predictive control.
This course runs in study tour mode during Summer Term 2, 2019. To undertake the study tour mode, students will need to manually enrol in the course, after contacting the course co-ordinator.
Availability2019 Course Timetables
- Semester 1 - 2019
This course replaces the following course(s): ELEC4410. Students who have successfully completed ELEC4410 are not eligible to enrol in ENGG3440.
On successful completion of the course students will be able to:
1. Understand the basics of state feedback based control system design including pole placement
2. Design linear quadratic control
3. Apply the principles of probabilistic modelling of noise
4. Understand the principle of minimum mean squared estimation and design Kalman filters for state estimation
5. Design LQG controllers
6. Design simple model predictive controllers
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
ENGG2440 Modelling and Control or MCHA2000 Mechatronic Systems.
Formal Examination: Final Examination
Quiz: Quizzes x 2
Tutorial / Laboratory Exercises: Laboratory Exercises x 6
Face to Face On Campus 2 hour(s) per Week for Full Term starting in week 3
Face to Face On Campus 3 hour(s) per Week for Full Term starting in week 1
Face to Face On Campus 1 hour(s) per Week for Full Term starting in week 2