Available in 2018

Course handbook

Description

This course is intended to equip students with an advanced understanding of control and estimation for nonlinear systems. Specifically, the course will provide an in-depth coverage of the key concepts of nonlinear systems and control theory such as stability, oscillatory behaviour, describing functions, Lyapunov theory and nonlinear model predictive control. It also covers advanced topics in Bayesian estimation for state-space models in detail, including the Kalman Filter (KF), the extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the Sequential Monte-Carlo techniques (particle filters).


Availability2018 Course Timetables

Callaghan

  • Semester 2 - 2018

Learning outcomes

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

1. Understand the common tools used to analyse the behaviour of a nonlinear system

2. Apply tools from Lyapunov theory to commonly encountered nonlinear systems in engineerin

3. Design and implement feedback control systems for regulating the output of a nonlinear system

4. Model uncertainty in Engineering systems

5. Apply Bayesian estimation methods to general estimation problems

6. Recognise the most appropriate implementation of Bayesian estimation for a given problem

7. Formulate decision problems in the presence of uncertainty


Content

This course will cover: • Review of concepts from systems theory • Stability and small gain theorem • Lyapunov theory • Oscillations and describing functions • Nonlinear internal model control • Nonlinear MPC • Bayesian estimation for state-space models; • The Kalman Filter • The extended Kalman Filter  • The Unscented Kalman Filter • Sequential Monte-Carlo techniques • Hypothesis testing • Parameter estimation   


Requisite

This course has similarities to ENGG4440. If you have completed ENGG4440 you cannot enrol in this course.


Assumed knowledge

ENGG6440 Linear Control and Estimation


Assessment items

Written Assignment: Assignments x 2

Tutorial / Laboratory Exercises: Laboratory Assignments x 7


Contact hours

Callaghan

Laboratory

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

Lecture

Face to Face On Campus 4 hour(s) per Week for Full Term