Course coordinator
Dr T. Thang Vo-Doan
Please email Dr Vo-Doan to arrange a consultation.
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Mech & Mine Engineering School
Coverage of various advanced topics in control systems engineering: (i) observers and state estimation, (ii) multivariable systems in the frequency domain, (iii) robust control, and (iv) model predictive control.
This course focuses on the principles and tools used for designing and analyzing feedback systems. It is specifically designed for students who are interested in utilizing feedback for control in mechanical, electrical, and electro-mechanical systems, although broader application domains are touched on to give a sense of the breadth of the discipline.
The primary emphasis of the course is on state-space control. It begins by introducing the process of modelling various systems, including mechanical, electrical, electromechanical, biological, social, and informational systems, using ordinary differential and difference equations. Several detailed examples are covered throughout the course to reinforce the concepts. The course then explores the dynamic behavior of models, covering stability and more complex nonlinear behavior. Linear systems are examined as a special case of nonlinear systems, and state-space control laws are discussed, including state feedback, output feedback, and estimators.
The concepts of reachability and observability are introduced, providing insights into the selection of actuators and sensors for both engineered and natural systems. Additionally, topics such as Kalman filtering and optimal control are covered. While these topics could be individual courses themselves, this course aims to present them in a concise manner that allows for practical application in engineering.
Throughout the course, students are encouraged to apply these concepts to control physical systems, fostering a hands-on approach to learning and applying the material.
Course requirements
Assumed background
A recommended background for this course includes the completion of an introductory control systems course, such as METR4201. Additionally, it is beneficial to have an understanding of mechanical and electrical system dynamics, which can be obtained through courses like MECH2210, MECH3200, ELEC2004, ELEC3004, and METR4201 (or equivalent courses).
It is worth noting that some students may find the application of mathematical concepts in this course challenging. It is assumed that students have some familiarity with basic tools of linear algebra, including matrices, vectors, eigenvalues, and eigenvectors. Similarly, knowledge of differential equations is required, specifically the concepts of homogeneous and particular solutions for linear ordinary differential equations in one variable. The course utilizes complex numbers and complex functions, so familiarity with these concepts is necessary. Additionally, students should be familiar with the Laplace transform and the concept of a transfer function.
Basic concepts in probability, including random variables and standard distributions, should also be familiar. While no prior familiarity with random processes is assumed, it can be helpful in understanding certain topics covered in the course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
METR4201 or METR7200
Incompatible
You can't enrol in this course if you've already completed the following:
METR7203
Course contact
Course staff
Lecturer
Dr T. Thang Vo-Doan
Dr Jasmin Martin
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The primary objective of this course is to enhance your understanding of the methods and principles employed in modern control engineering. By exploring the various concepts within control theory, the course aims to broaden your appreciation for the wide-ranging applications of these ideas and provide you with a comprehensive perspective on the field. Additionally, you will acquire the necessary knowledge to effectively utilize different design methods and gain practical experience in implementing control systems. By the end of the course, you will be equipped with the skills to confidently apply control engineering techniques and make informed decisions regarding their implementation.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Develop and analyse system models - Construct and analyse state space models for non-linear and linear, continuous and discrete dynamic systems from broadly-based domains such as motion control, information systems and biological systems.
LO2.
Develop and analyse system models - Employ simulation tools (i.e. Matlab and Simulink) to predict and analyse the dynamic behaviour of systems.
LO3.
Develop and analyse system models - Qualitatively analyse linear and non-linear dynamic systems through phase portraits, equilibrium points, limit cycles, stability, and Lyapunov methods.
LO4.
Develop and analyse system models - Predict the transient and steady state response of linear systems described by state space models when subjected to various inputs using analytical and numerical techniques.
LO5.
Develop and analyse system models - Construct discrete time system models from continuous system models by sampling and vice versa.
LO6.
Develop and analyse system models - Compose and study linearisations of non-linear systems by Jacobian linearisation and small value approximations for simple and complex systems.
LO7.
Design and implement state-feedback controllers - Construct different state space realisations of MIMO systems and understand their applications.
LO8.
Design and implement state-feedback controllers - Stabilise linear systems using state feedback.
LO9.
Design and implement state-feedback controllers - Analyse the properties of linear systems such as reachability and observability and understand how these properties impact on the ability to control a system.
LO10.
Design and implement state-feedback controllers - Employ state feedback design methods being cognizant of the tradeoffs that exist among the magnitude of the control inputs, the robustness of the systems to perturbations, and the closed loop performance of the system.
LO11.
Design and implement state-feedback controllers - Employ integral control action and know how it is realised through state feedback controllers.
LO12.
Design and implement output-feedback controllers - Analyse the observability of linear systems and recognize that if a system is observable it is possible to recover the state from measurements of the inputs and outputs of the system.
LO13.
Design and implement output-feedback controllers - Know how to design a controller with feedback from the observer state making use of the separation principle.
LO14.
Design and implement output-feedback controllers - Appreciate the role of feedforward in control system design.
LO15.
Design and implement output-feedback controllers - Understand how output feedback control laws are implemented in digital computers and through higher order software packages such as Matlab.
LO16.
Design and implement output-feedback controllers.
LO17.
Design and implement optimal control systems - Design and implement optimal controllers by minimisation of a quadratic cost in continuous and discrete time.
LO18.
Design and implement optimal control systems - Design and implement optimal estimators (aka Kalman filters) for linear systems and linearised systems in continuous and discrete time.
LO19.
Design and implement optimal control systems - Design steady state Linear Quadratic Gaussian regulators.
LO20.
Work as a team member in a group - Break down a control systems problem into manageable tasks and identify for each task who in the team is responsible, accountable, consulted and informed.
LO21.
Work as a team member in a group - Work with team members to deliver an outcome resolving technical and interpersonal issues and concerns as they raise effectively.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Problem Based Assignment | 20% (5% for each Assignment 1-4) |
Assignment 1 12/08/2024 4:00 pm Assignment 2 26/08/2024 4:00 pm Assignment 3 9/09/2024 4:00 pm Assignment 4 14/10/2024 4:00 pm |
| Presentation | Control System Implementation 1 | 30% |
16/09/2024 - 20/09/2024
Demo times will be made available for sign up. |
| Presentation | Control System Implementation 2 | 15% |
14/10/2024 - 18/10/2024
Demo times will be made available for sign up. |
| Examination |
Final Examination
|
35% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Problem Based Assignment
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20% (5% for each Assignment 1-4)
- Due date
Assignment 1 12/08/2024 4:00 pm
Assignment 2 26/08/2024 4:00 pm
Assignment 3 9/09/2024 4:00 pm
Assignment 4 14/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L06, L08, L12, L13, L14
Task description
The problem-based assignment series consists of a collection of concise assignments designed to help you grasp fundamental concepts. These concepts are subsequently put into practice through the Control Systems Implementation Projects.
Problem Based Assignment 1 (LO 1, 2) will require you to develop a Matlab/Simulink model of a system and conducting simulations to analyze the system's response to various inputs.
Problem Based Assignment 2 (LO 2, 3) will require you to utilize Matlab for generating and analyzing a phase portrait of a given system. Additionally, you will be tasked with determining a Lyapunov function for the system and using it to perform a qualitative analysis of system stability.
Problem Based Assignment 3 (LO 3, 4, 6, 8) involves the following tasks: (i) Creating a Simulink model of a balance system. (ii) Computing a linearization of the system and comparing it with numerical functions available in MATLAB. (iii) Designing a stabilizing control law for the linearized systems using state feedback. (iv) Developing a simulation that applies the designed stabilizing control law to demonstrate its local asymptotic stabilization of the system.
Problem Based Assignment 4 (LO 4, 6, 8, 12, 13, 14) aims to enhance your proficiency in utilizing the MATLAB Control Systems Toolbox, specifically in constructing state observers and state feedback controllers using the toolbox's available methods. The assignment emphasizes the application of state feedback for system stabilization and provides an opportunity to explore the balance between observer poles and feedback poles.
To access the detailed instructions and assessment criteria for each assignment, please refer to the assignment description provided on Blackboard.
Submission guidelines
Through turn-it-in via Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Feedback is provided to students following 7 calendar days.
A Student Access Plan (SAP) can only be used for a first extension. Extensions based on an SAP may be granted for up to seven (7) days, or the maximum number of days specified in the Electronic Course Profile (ECP), if it is less than seven (7) days. Any further extensions will require additional supporting documentation, such as a medical certificate.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Control System Implementation 1
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 30%
- Due date
16/09/2024 - 20/09/2024
Demo times will be made available for sign up.
- Learning outcomes
- L01, L02, L03, L05, L06, L08, L09, L10, L11, L20, L21
Task description
For this assignment, you will collaborate in groups to implement a control system using state feedback on physical hardware. Your performance will be evaluated through a demonstration of your control system and a presentation on its overall performance.
Each group will have the opportunity to sign up for a designated demo slot.
To access the specific details and requirements of the assignment, please refer to the assignment description available on Blackboard.
If, for whatever reason, you find that your group is not functioning effectively, please contact your Course Coordinator for support.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Students unable to attend their team's demo with an acceptable reason as described on https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension should apply for an extension.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Demo sessions are time limited.
Control System Implementation 2
- Mode
- Activity/ Performance
- Category
- Presentation
- Weight
- 15%
- Due date
14/10/2024 - 18/10/2024
Demo times will be made available for sign up.
- Learning outcomes
- L01, L02, L07, L08, L09, L11, L12, L13, L14, L15, L16, L17, L18, L19, L20, L21
Task description
For this assignment, you will collaborate in groups to implement an optimal control system using output feedback on physical hardware. Your performance will be assessed through a demonstration of your control system and a presentation on its overall performance.
Demo slots for the demonstrations will be made available for sign-up on Blackboard.
To access the specific details and requirements of the assignment, please refer to the assignment description available on Blackboard.
If, for whatever reason, you find that your group is not functioning effectively, please contact your Course Coordinator for support.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Students unable to attend their team's demo with an acceptable reason as described on https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension should apply for an extension.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Demo sessions are time limited.
Final Examination
- Hurdle
- Identity Verified
- Mode
- Written
- Category
- Examination
- Weight
- 35%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L04, L05, L06, L07, L09, L12, L13, L16, L17, L18
Task description
The final exam will test your understanding of the course material through problem solving.
The final exam will be closed book. Only Casio FX82 series or UQ approved (and labelled) calculator is allowed. You will be able to bring a companion mathomat with you, however NOT the WB3 edition.
Hurdle requirements
Identity verified assessment (IVA) will be through obtaining at least 40% of the available marks in the final exam. You must obtain a mark of at least 40% in Final Examination to receive a passing grade for the course.ﾠExam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Materials | A companion Mathomat, except the WB3 (Whiteboard Edition) |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0.00 - 29.99 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Overall grade 0.00 to 29.99% |
| 2 (Fail) | 30.00 - 44.99 |
Minimal evidence of achievement of course learning outcomes. Course grade description: Overall grade 30.0 to 44.99% |
| 3 (Marginal Fail) | 45.00 - 49.99 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Falls short of satisfying basic requirements for a Pass. Overall grade: 45-49.99% or less that 40% in the IVA requirement explained below. |
| 4 (Pass) | 50.00 - 64.99 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Satisfies all of the basic learning requirements for the course, such as knowledge of fundamental concepts and performance of basic skills; demonstrates sufficient quality of performance to be considered satisfactory or adequate or competent or capable in the course. Overall grade 50-64.99% and a minimum score of 40% in the IVA requirement explained below. |
| 5 (Credit) | 65.00 - 74.99 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: Demonstrates ability to use and apply fundamental concepts and skills of the course, going beyond mere replication of content knowledge or skill to show understanding of key ideas, awareness of their relevance, some use of analytical skills, and some originality or insight. Overall grade 65-74.99% and a minimum score of 40% in the IVA requirement explained below. |
| 6 (Distinction) | 75.00 - 84.99 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Demonstrates awareness and understanding of deeper and subtler aspects of the course, such as ability to identify and debate critical issues or problems, ability to solve non-routine problems, ability to adapt and apply ideas to new situations, and ability to invent and evaluate new ideas. Overall grade 75- 84.99% and a minimum score of 40% in the IVA requirement explained below. |
| 7 (High Distinction) | 85.00 - 100.00 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Demonstrates imagination, originality or flair, based on proficiency in all the learning objectives for the course; work is interesting or surprising or exciting or challenging or erudite. Overall grade 85 - 100% and a minimum score of 40% in the IVA requirement explained below. |
Additional course grading information
Grading Criteria
Specific grading criteria will be provided for each assessment item. These are available on Blackboard in the assessment folder.
ﾠ
Identity verified assessment.
Identity verified assessment (IVA) will be through obtaining at least 40% of the available marks in the final exam.
You must obtain a mark of at least 40% in Final Examination to receive a passing grade for the course.ﾠ
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
In accordance with University Policy, no credit is given towards assignments or practicals completed in previous attempts at this course.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
A. Course Outline (this document)
B. Matlab control systems toolbox homepage: http://www.mathworks.com/products/control/ ﾠ
Generic information on the MATLAB control systems tool box. This is a defacto industry standard for control systems modelling, analysis, and design. ﾠ
C. Control texts using MATLAB: http://www.mathworks.com/support/books/index.jsp?category=3 ﾠ
The Mathworks maintain a list of Matlab-based books on Control Systems Engineering and other general engineering topics. Most of these books have the associated m-files available for downloading - free. (The files are not much use without the corresponding textbook though!) ﾠ
D. Control tutorials for Matlab: https://ctms.engin.umich.edu/CTMS/index.php?aux=Home
There is some very useful self-study material on Control Systems linked to Matlab examples available here. The material includes an introduction to Matlab basics (for those of you who have forgotten!!), general control systems tutorial material and examples of how to apply Matlab to the solving of control system problems.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Problem-based learning |
Problem based learning workshops Weekly problem based workshops that develops understanding of the learning outcomes. Learning outcomes: L01, L02, L04, L05, L06, L07, L08, L09, L10, L11, L12, L13, L14, L15, L16, L17, L18, L19 |
General contact hours |
Contact Learning outcomes: L01, L02, L04, L05, L06, L07, L08, L09, L10, L11, L12, L13, L14, L15, L16, L17, L18, L19 |
|
Multiple weeks From Week 2 To Week 12 |
Practical |
Laboratory Implementation of control systems on laboratory equipment as a team Learning outcomes: L01, L02, L03, L04, L06, L07, L08, L09, L10, L11, L12, L13, L14, L15, L16, L17, L20, L21 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.