Advanced Control and Signal Processing

Course aim

1. To provide training in system modelling using state-space equations and applying optimal state estimation and optimal control techniques to state-space models.
2. To provide thorough understanding and knowledge of theory and design of estimation in signal processing.

Course content

• Model multivariable systems using state-space equations and linearisation techniques.
• Linear algebra for state-space methods.
• Concepts of controllability, stabilizability, observability, detectability, duality, canonical/Kalman decomposition.
• Design state-feedback controllers and Luenberger observers, and from them derive output-feedback controllers through the separation principle.
• Design optimal output-feedback controllers.
• Estimation Theory.
• Signal parameter estimation: e.g., maximum likelihood estimation.
• Signal waveform estimation: Wiener Filters.
• Spectral Estimation.

Textbook(s)

J.A. Rossiter 2018, A First Course in Predictive Control, 2nd, CRC Press

Prerequisite(s)

Corequisite(s)

Nil

Teaching method

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.

Assessment

Assignment, Examination, Practical

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
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Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
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* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.