AMME5520: Semester 1, 2020

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Units AMME5520 Semester 1 2020 [Normal day]

Unit of study_

AMME5520: Advanced Control and Optimisation

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

Overview

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
Prohibitions ?	None
Prerequisites ?	AMME3500 OR AMME9501 or AMME8501
Corequisites ?	None
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	Yes

Teaching staff

Coordinator	Ian Manchester, ian.manchester@sydney.edu.au
Lecturer(s)	Ian Manchester, ian.manchester@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1	10%	Week 06 Due date: 03 Apr 2020 at 23:00	n/a
Outcomes assessed:
In-semester test	Mid-semester quiz	5%	Week 08 Due date: 23 Apr 2020 at 09:00	n/a
Outcomes assessed:
Assignment	Lab report	10%	Week 11 Due date: 15 May 2020 at 23:00	n/a
Outcomes assessed:
Presentation	Lightning talk	5%	Week 13 Due date: 26 May 2020 at 11:00	n/a
Outcomes assessed:
Assignment	Major project	20%	Week 13 Due date: 29 May 2020 at 23:00	n/a
Outcomes assessed:
= hurdle task ? = 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	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 sydney.edu.au/students/guide-to-grades.

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.

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
Week 01	Introduction to the concept of optimal control, outline of the subject	Lecture and tutorial (4 hr)
Week 02	Finite-state optimal control and dynamic programming	Lecture and tutorial (4 hr)
Week 03	Path planning over a road network (dynamic programming and A*)	Lecture and tutorial (4 hr)
Week 04	Continuous state/time optimal control	Lecture and tutorial (4 hr)
Week 05	Linear systems and the linear quadratic regulator (LQR)	Lecture and tutorial (4 hr)
Week 06	LQR-based design of multivariable control systems	Lecture and tutorial (4 hr)
Week 07	State estimation and the Kalman filter	Lecture and tutorial (4 hr)
Week 08	Nonlinear state estimation and the extended Kalman filter	Lecture and tutorial (4 hr)
Week 09	System uncertainty and robust control	Lecture and tutorial (4 hr)
Week 10	Optimisation: Numerical Methods	Lecture and tutorial (4 hr)
Week 11	Model predictive control	Lecture and tutorial (4 hr)
Week 12	Reinforcement learning	Lecture and tutorial (4 hr)
Week 13	Lightning talks	Lecture and tutorial (4 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. 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
Learning outcomes

Responding to student feedback

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

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

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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