Course Title: Real Time Estimation and Control
Part A: Course Overview
Course Title: Real Time Estimation and Control
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
EEET2221 |
City Campus |
Undergraduate |
125H Electrical & Computer Engineering |
Face-to-Face |
Sem 2 2006, Sem 2 2007, Sem 2 2010, Sem 2 2012, Sem 2 2014, Sem 2 2015, Sem 2 2016 |
EEET2221 |
City Campus |
Undergraduate |
172H School of Engineering |
Face-to-Face |
Sem 2 2019, Sem 2 2020, Sem 2 2021, Sem 2 2022, Sem 2 2024 |
EEET2223 |
City Campus |
Postgraduate |
125H Electrical & Computer Engineering |
Face-to-Face |
Sem 2 2006, Sem 2 2007, Sem 2 2010, Sem 2 2012, Sem 2 2014, Sem 2 2015, Sem 2 2016 |
EEET2223 |
City Campus |
Postgraduate |
172H School of Engineering |
Face-to-Face |
Sem 2 2019, Sem 2 2020, Sem 2 2021, Sem 2 2022, Sem 2 2024 |
Course Coordinator: Professor Liuping Wang
Course Coordinator Phone: +61 3 992 52100
Course Coordinator Email: liuping.wang@rmit.edu.au
Course Coordinator Location: 10.8.8
Pre-requisite Courses and Assumed Knowledge and Capabilities
EEET2109 Control Systems or equivalent courses
Course Description
This course is prepared for the fourth year students or Masters degree students in the engineering field in large. It is particularly suitable if you are studying electrical engineering, electronic engineering, communication engineering, aerospace engineering, and mechanical engineering. It is to familiarise you with the model predictive control technologies that include multivariable control systems, constrained control and process optimization.
Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information (www1.rmit.edu.au/browse;ID=eyj5c0mo77631).
Objectives/Learning Outcomes/Capability Development
At undergraduate level this course develops the following Program Learning Outcomes:
1.3 In-depth understanding of specialist bodies of knowledge within the engineering discipline.
2.1 Application of established engineering methods to complex engineering problem solving.
2.2 Fluent application of engineering techniques, tools and resources
At postgraduate level this course develops the following Program Learning Outcomes:
- High levels of technical competence in the field
- Be able to apply problem solving approaches to work challenges and make decisions using sound engineering methodologies
Learning Outcomes
- To introduce advanced topics in discrete time systems, multivariable control systems, constrained control and process optimization, leading to skills in system simulation, multivariable control system design and analysis, and practical implementation of control systems.
- Well developed oral and written communications.
- Good teamwork
- Independent and lifelong learning
Overview of Learning Activities
- Reading text books
- Attending lectures
- Participating in tutorials
- participating in laboratory activities
Overview of Learning Resources
The course will be based on the textbook ’Model predictive control system design and implementation using MATLAB’ by Liuping Wang.
The textbook is free to RMIT students and it can be downloaded through the link below.
The link is: https://login.ezproxy.lib.rmit.edu.au/login?url=http://dx.doi.org/10.1007/978-1-84882-331-0
This takes you straight to the electronic copy of the textbook, so you can download it for free.
The weblink includes EzProxy as part of the string, so when off campus, you will be taken to a login screen first, then to SpringerLink.
Overview of Assessment
☒This course has no hurdle requirements.
Overview of Assessment
Assessment Task 1:
Final Open Book, Mini Project Based Assessment
Weighting: 50%
CLOs: 1
Assessment Task 2
Week 6 Assignment
Weighting: 15%
CLOs: 3, 4
Assessment Task 3
Laboratory report
Weighting: 20%
CLOs: 2, 3
Assessment Task 4:
Tutorial and Laboratory activities
Weighting: 15%
CLOs: 2