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Unit of study_

AMME5520: Advanced Control and Optimisation

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Unit code AMME5520
Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
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

Coordinator Ian Manchester,
Lecturer(s) Ian Manchester,
Type Description Weight Due Length
Final exam hurdle task Final exam
50% Formal exam period 2 hours
Outcomes assessed: LO2 LO4 LO3
Assignment Assignment 1
10% Week 06
Due date: 03 Apr 2020 at 23:00
Outcomes assessed: LO1 LO2 LO3 LO4
In-semester test Mid-semester quiz
5% Week 08
Due date: 23 Apr 2020 at 09:00
Outcomes assessed: LO3 LO4
Assignment group assignment Lab report
10% Week 11
Due date: 15 May 2020 at 23:00
Outcomes assessed: LO3 LO4
Presentation Lightning talk
5% Week 13
Due date: 26 May 2020 at 11:00
Outcomes assessed: LO1
Assignment Major project
20% Week 13
Due date: 29 May 2020 at 23:00
Outcomes assessed: LO1 LO2 LO3 LO4
hurdle task = hurdle task ?
group assignment = group assignment ?

Assessment summary

  • 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

For more information see guide to grades.

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.

Academic integrity

The Current Student website  provides information on academic integrity 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 integrity breaches seriously.  

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
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.

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

This section outlines changes made to this unit following staff and student reviews.

Increased worked problems and tutorials. Tighter link between lab and lecture.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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