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 ?	ELEC2302 AND (MATH2061 OR MATH2067 OR MATH2021 OR MATH2961 OR AMME2000)
Corequisites ?	None
Prohibitions ?	AMME3500
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	Yes

Teaching staff

Coordinator	Yash Shrivastava, yash.shrivastava@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam End of Semester Exam	60%	Formal exam period	2 hours
Outcomes assessed:
Participation	Labs	7%	Multiple weeks	n/a
Outcomes assessed:
Participation	Tutorials	8%	Multiple weeks	n/a
Outcomes assessed:
Small test	Mid-term exam Midterm Exam	25%	Week 09 Due date: 13 Oct 2021 at 11:00 Closing date: 13 Oct 2021	70 minutes
Outcomes assessed:
= group assignment ? = Type C final exam ?

Assessment summary

Tutorials: There will be 8 tutorials (of 2 hours each) during the semester. Tutorials will include analytical problem solving sessions on the material covered in the lectures, and computer aided solution/illustration. These sessions will give you the opportunity to explore the concepts in detail and are very helpful in understanding the material covered in the lecture.
Labs: There will be 4 laboratories (of 3 hours each) during the semester. Laboratories are designed to introduce you to basic feedback control concepts and measurements. They will require you to do system identification for lab equipment, and implement/test a few standard controllers for it, and model and simulate dynamic systems, and controllers in Matlab. Students will work in groups of two or three, but will submit individual reports.
Mid-term exam: The midterm exam is scheduled to give you a practice run for the final exam. It will be of the same format as the final exam (but of shorter duration). This exam will be closed-book and closed-notes. It will test your conceptual understanding of the lecture material and tutorials.
Final exam: This exam will be closed-book and closed-notes. It will test your conceptual understanding of the lecture material and tutorials.

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.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Students need to spend roughly 3 hours each week on independent study to deeply engage with the material covered in that week	Independent study (39 hr)
Week 01	Introduction and review of Laplace Transform	Lecture and tutorial (2 hr)
Week 02	Modeling of physical systems	Lecture and tutorial (5 hr)
Week 03	Modeling of physical systems and linearization of nonlinear systems	Lecture and tutorial (5 hr)
Week 04	Time response of linear systems	Lecture and tutorial (5 hr)
Week 05	System reduction	Lecture and tutorial (5 hr)
Week 06	Stability of linear systems, Routh-Hurwitz criterion, and steady state errors	Lecture and tutorial (5 hr)
Week 07	Steady state errors, sensitivity, and root locus techniques	Lecture and tutorial (5 hr)
Week 08	Controller design using root locus	Lecture and tutorial (5 hr)
Week 09	Lecture devoted to Midterm Exam	Lecture and tutorial (5 hr)
Week 10	Controller design using root locus continued	Lecture and tutorial (5 hr)
Week 11	Frequency response and Bode plots	Lecture and tutorial (5 hr)
Week 12	Nyquist criterion for stability and controller design using frequency response techniques	Lecture and tutorial (5 hr)
Week 13	Controller design using state space techniques	Lecture and tutorial (5 hr)

Attendance and class requirements

Tutorials: Tutorials are devoted to practicing basic concepts covered in the lectures, and understanding how more complex tasks can be handled by putting these basic concepts together. The focus is on active learning, and group work and discussion is encouraged. Students will also get to present their solutions to the rest of the class.
Laboratories: Labs are devoted to hands-on experience with real control systems. Students will do the modeling, make measurements, and design control schemes for the lab equipment. They will also present their results in the format of lab reports.
Independent study: Students will need to do some preparation for tutorials and labs; they may need to read the texts and other references to fully master the basic concepts covered in the lectures.

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

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Norman S. Nise, Control Systems Engineering (eigth). Wiley, 2019. ISBN 9781119594352.

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. recognise the limits of the information presented in the lectures, and target information searches through varied sources and formats, so as to synthesise information relevant to the specific topic at hand
LO2. produce written and oral presentations in the form of lab reports and tutorial presentations
LO3. work in a team to discuss and draw upon the ideas and knowledge of others, to solve and present tutorial problems, and conduct lab experiments
LO4. conduct lab experiments, and take measurements to perform a model identification for a particular engineering problem
LO5. design and test feedback control schemes for the lab equipment to achieve different performance requirements
LO6. analyse the dynamic response of linear time invariant systems, and the role of system poles and zeros on it
LO7. simplify complex systems consisting of interconnections of several linear subsystems
LO8. determine the stability of feedback systems and their steady state performance
LO9. design simple controllers to achieve stability and transient performance requirements, using root locus, frequency response, and state space techniques
LO10. Model physical systems (e.g. electrical, mechanical, and electromechanical systems) using state space, differential equations, and transfer functions.

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.

Learning activities changed back to 13 weeks.

Disclaimer

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

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ELEC3304: Control

Semester 2, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect