Undergraduate - UnitMAE3408 - Aerospace control

This unit entry is for students who completed this unit in 2012 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

Level	Undergraduate
Faculty	Faculty of Engineering
Offered	Clayton Second semester 2012 (Day)
Coordinator(s)	Dr Chao Chen

Synopsis

This unit covers control theory for aerospace systems with the use of state-space techniques. The state-space of dynamic systems and resulting equations are covered. Concepts of controllability, observability, detectability and the state-transition matrix follow. Classical control concepts including root-locus and frequency-response techniques are set in context of their importance in robust control. Compensators for aerospace systems with full and reduced-order linear observers, parametric optimization. Linear quadratic optimal controllers. The equations of motion for dynamic systems with controllers determined with computational analysis in Matlab.

Outcomes

An understanding of the role of linear algebra in engineering dynamics and controls.
An understanding of modern control theory and its use in aerospace systems.
The concepts of controllability, stabilizability, observability, and detectability and their use in controllers.
An appreciation for optimization techniques, particularly those applied to optimum controllers.
The knowledge of where to go to learn more beyond the content of the course on related and more advanced topics.
A well-rounded individual ability to conduct control system analysis and design via independent hand and computer calculations in Matlab.
An individual ability to determine the behaviour of simple aerospace dynamic systems and develop strategies to control that behavior, qualitatively and quantitatively.
An individual ability to determine the state-transfer matrix, determine the SISO transfer function, design regulators and full/reduced-order/linear quadratic control observers.
An individual ability to optimize a dynamic system based on the mathematical model of the system and linear optimization theory.
Presenting student work in a cogent and concise manner.

Assessment

Examination (3 hours): 70%
Practice classes 20%
Computer laboratory exercise: 10%

Chief examiner(s)

Professor Chris Davies

Contact hours

Six hours of contact time per week - usually 3 hours lectures and 3 hours practice sessions or laboratories as well as 6 hours of private study per week

Prerequisites

MAE3404