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AMME4500: Guidance, Navigation and Control (2013 - Semester 1)

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Unit: AMME4500: Guidance, Navigation and Control (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior Advanced
Faculty/School: School of Aerospace, Mechanical & Mechatronic Engineering
Unit Coordinator/s: A/Prof Manchester, Ian
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: AMME3500.
Brief Handbook Description: This unit introduces engineering design via optimization, 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 optimization frameworks including linear and quadratic programming (LP and QP), dynamic programming (DP), path planning with A*, 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: Students have an interest and a 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: AMME4500 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 given GNC tasks that will develop their skills in algorithmic implementation to meet the requirements of the problem. Design (Level 4)
Students will be developing a deep level of understanding of the theoretical basis of GNC algorithms as well as practical skills in their implementation. Engineering/IT Specialisation (Level 5)
Students will be reading the latest reference articles on GNC as applied to autonomous vehicles, and conducting their own literature search. Information Seeking (Level 4)

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

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.

Design (Level 4)
1. An ability to implement simple path generation algorithms, controllers and decision metrics for an autonomous system, in order to meet specific mission objectives.
Engineering/IT Specialisation (Level 5)
2. 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.
3. 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.
Information Seeking (Level 4)
4. An ability to approach research papers in a professional and research orientated manner, and to conduct critical reviews of these papers.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment No 10.00 Week 5 1, 2, 3, 4,
2 Mid-semester quiz No 10.00 Week 8 2, 3,
3 Assignment Yes 30.00 Week 12 1, 2, 3, 4,
4 Final exam No 50.00 Exam Period 1, 2, 3,
Assessment Description: Assignment 1: Development of a simple path planning system for an autonomous vehicle.

Assignment 2: Case study in optimal control design for a complex dynamical system.
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 . 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: All university policies can be found at

Policies and request forms for the Faculty of Engineering and IT can be found on the forms and policies page of the faculty website at
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.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar

Week Description
Week 1 Introduction to control and guidance via optimization, and outline of course
Week 2 Path planning over a road network (Dynamic Programming and A*)
Week 3 Path planning over a road network (Dynamic Programming and A*)
Case Studies
Week 4 Optimisation
Week 5 Optimisation
Case Studies
Assessment Due: Assignment
Week 6 Control of Multivariable Systems
Case Studies
Week 7 Optimal Control
Week 8 Optimal Control
Case Studies
Assessment Due: Mid-semester quiz
Week 9 State Estimators
Week 10 Kalman Filters
Week 11 Real-Time Optimisation and Model Predictive Control
Week 12 Advanced Case Studies
Assessment Due: Assignment
Week 13 Case Studies and Review
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
Mechatronic (till 2014) 2011, 2012, 2013, 2010
Mechatronic Engineering / Arts 2011, 2012, 2013
Mechatronic (Space) (till 2014) 2011, 2012, 2010, 2013
Mechatronic Engineering (Space) / Arts 2011, 2012, 2013
Aeronautical (till 2014) 2010, 2011, 2012, 2013
Aeronautical Engineering / Arts 2011, 2012, 2013
Aeronautical Engineering / Science 2011, 2012, 2013
Aeronautical (Space) (till 2014) 2010, 2011, 2012, 2013
Aeronautical Engineering (Space) / Arts 2011, 2012, 2013
Aeronautical Engineering (Space) / Science 2011, 2012, 2013
Mechatronic Engineering / Commerce 2010, 2011, 2012, 2013
Mechatronic Engineering / Medical Science 2011, 2012, 2013
Mechatronic Engineering / Project Management 2012, 2013
Mechatronic Engineering / Science 2011, 2012, 2013
Mechatronic Engineering (Space) / Medical Science 2011, 2012, 2013
Mechatronic Engineering (Space) / Project Management 2012, 2013
Mechatronic Engineering (Space) / Science 2011, 2013
Aeronautical Engineering / Commerce 2010, 2011, 2012, 2013
Aeronautical Engineering / Law 2010
Mechatronic Engineering (Space) / Law 2013

Course Goals

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

Attribute Practiced Assessed
Design (Level 4) Yes 30%
Engineering/IT Specialisation (Level 5) Yes 60%
Information Seeking (Level 4) Yes 10%

These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 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.