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Unit of study_

AMME8520: Advanced Control and Optimisation

2024 unit information

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

Managing faculty or University school:

Aerospace, Mechanical and Mechatronic

Code AMME8520
Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
Prerequisites:
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None
Corequisites:
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None
Prohibitions:
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AMME5520
Assumed knowledge:
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Strong understanding of feedback control systems, specifically in the area of system modelling and control design in the frequency domain

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.

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 1 2024
Normal day Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 1 2021
Normal day Remote
Semester 1 2022
Normal day Camperdown/Darlington, Sydney
Semester 1 2022
Normal day Remote
Semester 1 2023
Normal day Camperdown/Darlington, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.