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Unit of study_

AMME5520: Advanced Control and Optimisation

This unit introduces engineering design via optimisation, i. e. finding the "best possible" solution to a particular problem. For example, an autonomous vehicle must find the fastest route between two locations over a road network; a biomedical sensing device must compute the most accurate estimate of important physiological parameters from noise-corrupted measurements; a feedback control system must stabilise and control a multivariable dynamical system (such as an aircraft) in an optimal fashion. The student will learn how to formulate a design in terms of a "cost function", when it is possible to find the "best" design via minimization of this "cost", and how to do so. The course will introduce widely-used optimisation frameworks including linear and quadratic programming (LP and QP), dynamic programming (DP), path planning with Dijkstra's algorithm, A*, and probabilistic roadmaps (PRMs), state estimation via Kalman filters, and control via the linear quadratic regulator (LQR) and Model Predictive Control (MPC). There will be constant emphasis on connections to real-world engineering problems in control, robotics, aerospace, biomedical engineering, and manufacturing.


Academic unit Aerospace, Mechanical and Mechatronic
Unit code AMME5520
Unit name Advanced Control and Optimisation
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

AMME3500 OR AMME9501 or AMME8501
Assumed knowledge

Strong understanding of feedback control systems, specifically in the area of system modelling and control design in the frequency domain.

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Ian Manchester,
Lecturer(s) Ian Manchester ,
Type Description Weight Due Length
Final exam (Open book) Type C final exam hurdle task Final Exam
Final Exam (Type C). 30% multiple-choice.
30% Formal exam period 2 hours
Outcomes assessed: LO2 LO4 LO3
Online task Quiz 1
Short online quiz
5% Week 05 1hr
Outcomes assessed: LO3 LO4
Assignment Project: Part 1
Written report submission based on algorithm coding and analysis
20% Week 07 n/a
Outcomes assessed: LO1 LO4 LO3 LO2
Online task Quiz 2
Short online quiz
5% Week 09 1hr
Outcomes assessed: LO3 LO4
Assignment Project: Part 2
Written report submission based on algorithm coding and analysis
30% Week 11 n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Presentation Lightning talk
Short in-class presentation
10% Week 13 5 min
Outcomes assessed: LO1
hurdle task = hurdle task ?
Type C final exam = Type C final exam ?
  • Assignment 1: Assignment 1 is a matlab exercise in optimal path planning and feedback control.
  • Mid-semester quiz: The mid-semester quiz tests knowledge of the fundamental concepts and mathematical techniques of the first half of the subject.
  • Major project: The major project builds upon assignment 1 to a complete autonomous vehicle planning, localisation, and control system.
  • Lightning talk: The lightning talk is a short presentation based on research of advanced methods related to this subject.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Multiple weeks Working on assessments, solving tutorial and practice problems, reading of lecture notes, study for exam Independent study (80 hr) LO1 LO2 LO3 LO4
Week 01 Introduction to the concept of optimal control, outline of the subject Lecture and tutorial (4 hr) LO4
Week 02 Finite-state optimal control and dynamic programming Lecture and tutorial (4 hr) LO3 LO4
Week 03 Path planning over a road network (dynamic programming and A*) Lecture and tutorial (4 hr) LO3 LO4
Week 04 Continuous state/time optimal control Lecture and tutorial (4 hr) LO3 LO4
Week 05 Linear systems and the linear quadratic regulator (LQR) Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 06 LQR-based design of multivariable control systems Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 07 State estimation and the Kalman filter Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 08 Nonlinear state estimation and the extended Kalman filter Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 09 System uncertainty and robust control Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 10 Optimisation: Numerical Methods Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 11 Model predictive control Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 12 Reinforcement learning Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 13 Lightning talks Lecture and tutorial (4 hr) LO1

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Lecture notes provided

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Approach research papers in a professional and research-orientated manner, and conduct critical reviews of these papers
  • LO2. Implement simple path generation algorithms, controllers and decision metrics for an autonomous system in order to meet specific mission objectives
  • LO3. Understand a number of different path generation and control algorithms implemented in autonomous systems and how they are linked to optimality criteria, platform stability and vehicle constraints.
  • LO4. Understand how "cost functions" are used to define mission objectives in a mathematical form, so that autonomous systems can make decisions about their next action.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
The subject has been redesigned to spread assessments throughout the semester, and more practice material.


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