Outline

Overview

This unit is mainly concerned with the application of feedback control to continuous-time, linear time-invariant systems. It aims to give the students an appreciation of the possibilities in the design of control and automation in a range of application areas. The concepts learnt in this unit will be made use of heavily in many units of study in the areas of communication, control, electronics, and signal processing. The following specific topics are covered: Modelling of physical systems using state space, differential equations, and transfer functions, dynamic response of linear time invariant systems and the role of system poles and zeros on it, simplification of complex systems, stability of feedback systems and their steady state performance, Routh-Hurwitz stability criterion, sketching of root locus and controller design using the root locus, Proportional, integral and derivative control, lead and lag compensators, frequency response techniques, Nyquist stability criterion, gain and phase margins, compensator design in the frequency domain, state space design for single input single-output systems, pole placement state variable feedback control and observer design.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ELEC5735
Assumed knowledge ?	Specifically the following concepts are assumed knowledge for this unit: familiarity with basic Algebra, Differential and Integral Calculus, Physics; solution of linear differential equations, Matrix Theory, eigenvalues and eigenvectors; linear electrical circuits, ideal op-amps; continuous linear time-invariant systems and their time and frequency domain representations, Laplace transform, Fourier transform
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Daniel Quevedo, daniel.quevedo@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Students may bring a personal double-sided A4 size cheat sheet	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	Lab design demonstration with individual Q&A Lab design demonstration of group work, followed by individual Q&A. Assessment will occur during the scheduled practical session of each group. Groups are of two students.	20%	Week 05 Due date: 04 Sep 2026 at 23:59	10 minutes	AI prohibited
Outcomes assessed:
Practical skill	Open Design Project An open design project for control systems using state space methods presented during the lectures.	20%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 20 Nov 2026	6 pages in IEEE double-column format.	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Lab design demonstration with individual Q&A: Students present their control designs on the laboratory equipment (5 minutes). This is followed by 5 minutes of Q&A so that the students can demonstarte their learning. The weighing is 20%.
Open Design Project: The design project tests the knowledge and skills taught in lectures/tutorials/practicals, and additionally a broader range of abilities, including critical reasoning about mathematical modelling, experiment design, data visualisation, drawing conclusions from mathematics and empirical results, and written communication skills. The weighing is 20%.
Final exam: The exam is closed-book, but students may bring a personal double-sided A4 size cheat sheet. This is a Hurdle task. Passing the exam is a necessary, but not sufficient condition for passing the unit.

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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard, and/or fail the hurdle exam.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The Assessment Procedures 2011 provide that any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Fundamental Notions and System Modelling	Lecture (2 hr)
	Introduction to Matlab/Simulink and the laboratory setup	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (7 hr)
Week 02	Simple Forms of Control	Lecture (2 hr)
	Simple Forms of Control	Tutorial (1.5 hr)
	Simple Forms of Control	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (5.5 hr)
Week 03	PID Control	Lecture (2 hr)
	PID Control	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 04	Time-Domain Design of Linear Controllers in Transfer Function Form	Lecture (2 hr)
	Time-Domain Design of Linear Controllers in Transfer Function Form	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 05	Frequency Domain Analysis of Control Loops	Lecture (2 hr)
	Frequency Domain Analysis of Control Loops	Tutorial (1.5 hr)
	PID Control	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (5.5 hr)
Week 06	Frequency Domain Control Design	Lecture (2 hr)
	Frequency Domain Control Design	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 07	Frequency Domain Control Design	Lecture (2 hr)
	Frequency Domain Control Design	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 08	System Modelling and State Space Descriptions	Lecture (2 hr)
	System Modelling and State Space Descriptions	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 09	State Feedback Control Design	Lecture (2 hr)
	State Feedback Control Design	Tutorial (1.5 hr)
	Open Design Project	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (5.5 hr)
Week 10	State Feedback Control Design	Lecture (2 hr)
	State Feedback Control Design	Tutorial (1.5 hr)
	Open Design Project	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (5.5 hr)
Week 11	State Observers and Output Feedback	Lecture (2 hr)
	State Observers and Output Feedback	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)
Week 12	Design Studies	Lecture (2 hr)
	Design Studies	Tutorial (1.5 hr)
	Open Design Project	Practical (3 hr)
	Study material, individually or in a group.	Self-directed learning (5.5 hr)
Week 13	Revision	Lecture (2 hr)
	Revision	Tutorial (1.5 hr)
	Study material, individually or in a group.	Self-directed learning (8.5 hr)

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

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.

Outcomes
Graduate qualities

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

LO1. mathematically model electrical, mechanical and other systems and determine their response characteristics based on the physical properties of the system.
LO2. understand how desired specifications of a system such as stability, overshoot, rise time, the time constant of a system, natural frequency and damping ratio can be represented mathematically and how they depend on system parameters.
LO3. demonstrate an ability to design controllers and meet specifications using frequency domain approaches and state space methods. Understand the relative strengths and weaknesses of each technique.
LO4. understand the role of feedback in providing robustness to modelling uncertainty and external disturbances.
LO5. analyse and design control loops using Matlab and Simulink software tools.

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
Learning outcomes

Responding to student feedback

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

In response to feedback from students and discussion with a number of experts in the field, we have re-designed the unit contents adopting more contemporary textbooks and approaches. Practicals were redesigned to deliver a fit-for-purpose and pedagogically effective laboratory experience.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC9304: Control

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links