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AMME5520: Advanced Control and Optimisation (2019 - Semester 1)

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Unit: AMME5520: Advanced Control and Optimisation (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Aerospace, Mechanical & Mechatronic Engineering
Unit Coordinator/s: A/Prof Manchester, Ian
Session options: Semester 1
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: AMME3500 OR AMME9501 OR AMME8501.
Brief Handbook Description: 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 stabilize 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.
Assumed Knowledge: Strong understanding of feedback control systems, specifically in the area of system modelling and control design in the frequency domain.
Lecturer/s: A/Prof Manchester, Ian
Timetable: AMME5520 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Tutorial 2.00 1 13
3 Independent Study 4.00 1 13
4 Research 1.00 1 13
T&L Activities: Tutorial: Tutorials are constructed to provide a deeper understanding of the theoretical material. This will take the form of algorithm implementation in Matlab.

Independent Study: Students will undertake independent study focussed primarily on completion of assignment work, refelction of theoretical material, and preperation for the quiz.

Research: Students will be researching up on various reference articles provided and providing feedback on the material read in the form of a paper review.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students will be developing a deep level of understanding of the theoretical basis of GNC algorithms as well as practical skills in their implementation. (2) Engineering/ IT Specialisation (Level 5)
Students will be given GNC tasks that will develop their skills in algorithmic implementation to meet the requirements of the problem. (4) Design (Level 4)
Students will be reading the latest reference articles on GNC as applied to autonomous vehicles, and conducting their own literature search. (6) Communication and Inquiry/ Research (Level 4)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 4)
1. An ability to approach research papers in a professional and research orientated manner, and to conduct critical reviews of these papers.
(4) Design (Level 4)
2. An ability to implement simple path generation algorithms, controllers and decision metrics for an autonomous system, in order to meet specific mission objectives.
(2) Engineering/ IT Specialisation (Level 5)
3. An understanding of 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.
(1) Maths/ Science Methods and Tools (Level 4)
4. An understanding of how "cost functions" are used to define mission objectives in a mathematical form, so that autonomous systems can make decisions about their next action.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment 1* No 10.00 Week 6 1, 2, 3, 4,
2 Mid-semester quiz* No 5.00 Week 8 3, 4,
3 Lab report* Yes 10.00 Week 13 3, 4,
4 Major project* No 20.00 Week 13 1, 2, 3, 4,
5 Lightning Talk* No 5.00 Week 13 1,
6 Final exam No 50.00 Exam Period 2, 3, 4,
Assessment Description: * indicates an assessment task which must be repeated if a student misses it due to special consideration

- Assignment 1 is a matlab exercise in optimal path planning and feedback control.

- The mid-semester quiz tests knowledge of the fundamental concepts and mathematical techniques of the first half of the subject.

- The major project builds upon Assignment 1 to a complete autonomous vehicle planning, localisation, and control system.

- The Lightning talk is a short presentation based on research of advanced methods related to this subject.

Students MUST pass the final exam in order to pass the subject.
Assessment Feedback: Feedback from the lecturer and tutor will be based on ongoing assessment and will be continuous throughout the semester.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
Online Course Content: Blackboard LMS

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction to control and guidance via optimization, and outline of course
Week 2 Finite-State Optimal Control and Dynamic Programming
Week 3 Path planning over a road network (Dynamic Programming and A*)
Week 4 Continuous State/Time Optimal Control
Week 5 Linear Systems and the Linear Quadratic Regulator (LQR)
Week 6 LQR-based Design of Multivariable Control Systems
Assessment Due: Assignment 1*
Week 7 State Estimation and the Kalman Filter
Week 8 Nonlinear State Estimation and the Extended Kalman Filter
Assessment Due: Mid-semester quiz*
Week 9 System Uncertainty and Robust Control
Week 10 Convex Optimisation
Week 11 Real-Time Optimisation and Model Predictive Control
Week 12 Approximate Dynamic Programming and Reinforcement Learning
Week 13 Lightning Talks
Assessment Due: Lab report*
Assessment Due: Major project*
Assessment Due: Lightning Talk*
Exam Period Assessment Due: Final exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Aeronautical (till 2014) 2014
Aeronautical Engineering / Commerce 2014
Aeronautical Engineering / Science 2014
Aeronautical Engineering (Space) / Commerce 2014
Aeronautical (Space) (till 2014) 2014
Aeronautical Engineering (Space) / Arts 2014
Aeronautical Engineering (Space) / Science 2014
Aeronautical 2019, 2020
Aeronautical / Commerce 2015
Aeronautical (Space) / Arts 2015
Aeronautical (Space) / Commerce 2015
Aeronautical (Space) / Science 2015
Mechanical Mid-Year 2016, 2017, 2018, 2019, 2020
Mechanical/ Project Management 2019, 2020
Mechanical 2015, 2016, 2017, 2018, 2019, 2020
Mechanical / Arts 2015, 2016, 2017, 2018, 2019, 2020
Mechanical / Commerce 2015, 2016, 2017, 2018, 2019, 2020
Mechanical / Music Studies 2016, 2017
Mechanical / Project Management 2016, 2017, 2018
Mechanical / Science 2015, 2016, 2017, 2018, 2019, 2020
Mechanical/Science(Health) 2018, 2019, 2020
Mechanical / Law 2016, 2017, 2018, 2019, 2020
Mechanical (Space) 2015
Mechanical (Space) / Arts 2015
Mechanical (Space) / Commerce 2015
Mechanical (Space) / Science 2015
Mechatronic Mid-Year 2016, 2017, 2018, 2019, 2020
Mechatronic/ Project Management 2019, 2020
Mechatronic 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic / Arts 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic / Commerce 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic / Medical Science 2015, 2016, 2017
Mechatronic / Music Studies 2016, 2017
Mechatronic / Project Management 2015, 2016, 2017, 2018
Mechatronic / Science 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic/Science (Health) 2018, 2019, 2020
Mechatronic / Law 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic (Space) 2015
Mechatronic (Space) / Arts 2015
Mechatronic (Space) / Commerce 2015
Mechatronic (Space) / Medical Science 2015
Mechatronic (Space) / Project Management 2015
Mechatronic (Space) / Science 2015
Mechatronic (Space) / Law 2015
Mechanical (till 2014) 2014
Mechanical Engineering / Arts 2014
Mechanical Engineering / Commerce 2014
Mechanical Engineering / Science 2014
Mechanical (Space) (till 2014) 2014
Mechanical Engineering (Space) / Arts 2014
Mechanical Engineering (Space) / Science 2014
Mechatronic (till 2014) 2014
Mechatronic Engineering / Arts 2014
Mechatronic Engineering / Commerce 2014
Mechatronic Engineering / Medical Science 2014
Mechatronic Engineering / Project Management 2014
Mechatronic Engineering / Science 2014
Mechatronic (Space) (till 2014) 2014
Mechatronic Engineering (Space) / Arts 2014
Mechatronic Engineering (Space) / Commerce 2014
Mechatronic Engineering (Space) / Medical Science 2014
Mechatronic Engineering (Space) / Project Management 2014
Mechatronic Engineering (Space) / Science 2014
Mechatronic Engineering (Space) / Law 2014
Master of Engineering 2014, 2015, 2016, 2017, 2018, 2019, 2020
Mechanical/Science (Medical Science Stream) 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Aerospace) 2019, 2020
Master of Professional Engineering (Accelerated) (Biomedical) 2019, 2020
Master of Professional Engineering (Aerospace) 2014, 2015, 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Mechanical) 2019, 2020
Master of Professional Engineering (Biomedical) 2014, 2015, 2016, 2017, 2018, 2019, 2020
Master of Professional Engineering (Mechanical) 2014, 2015, 2016, 2017, 2018, 2019, 2020
Mechatronic/Science (Medical Science Stream) 2018, 2019, 2020
Aeronautical Mid-Year 2019, 2020
Aeronautical/ Project Management 2019, 2020
Aeronautical / Arts 2019, 2020
Aeronautical / Law 2019, 2020
Mechanical / Medical Science 2016, 2017

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(6) Communication and Inquiry/ Research (Level 4) Yes 12.5%
(4) Design (Level 4) Yes 27.5%
(3) Problem Solving and Inventiveness (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 5) Yes 25%
(1) Maths/ Science Methods and Tools (Level 4) No 35%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.