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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

AMME5520: Advanced Control and Optimisation

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.

Code AMME5520
Academic unit Aerospace, Mechanical and Mechatronic
Credit points 6
AMME3500 OR AMME9501 or AMME8501
Assumed knowledge:
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 outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.