41014 Sensors and Control for Mechatronic Systems
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 48622 Embedded Mechatronics Systems
Recommended studies:
basic knowledge of classical control techniques
Description
The objectives of this subject are to develop the student's theoretical and practical understanding on active and passive sensing and feedback control techniques in mechatronic and robotic systems, ability to select and evaluate sensors, process the sensor data, and apply computer-based tools for practical control system design using the sensory information. Topics include visual imaging and image processing, infrared imaging, time of flight (TOF) measurements, detection and tracking, state-space modelling of linear systems, stability, controllability and observability, linear quadratic control, observer design, H-infinity control, and model predictive control. Case studies of engineering applications are used to illustrate and examine these concepts.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Implement sensors and processing techniques and control strategies. (D.1) |
|---|---|
| 2. | Apply knowledge of image processing and active sensor processing. (D.1) |
| 3. | Apply knowledge of advanced control techniques. (D.1) |
| 4. | Design sensors, signal processing and control solutions to practical problems. (C.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 1.4. Discernment of knowledge development and research directions within the engineering discipline.
- 2.2. Fluent application of engineering techniques, tools and resources.
- 2.3. Application of systematic engineering synthesis and design processes.
- 3.4. Professional use and management of information.
Teaching and learning strategies
The teaching and learning strategies focus on:
- Online preparatory materials for in-class activities
- Practical tutorial problems for students to apply the learned techniques
- Practical project problems for students to design an integrated system using the sensors and control techniques
- Collaborative group work and active hands on work
Content (topics)
- Passive Sensors: Visual imaging and image processing, Infrared imaging
- Basics of Active Sensors: Operating principles, time of flight (TOF) measurement & imaging, sensor data processing, Detection and tacking
- State space modelling of dynamic systems: linear continuous time model, linear discrete time model,
- Fundamental issues in control system design: Lyapunov stability, controllability, observability, stabilization, pole assignment.
- Linear optimal control: linear quadratic control, observer design
- H-infinity control
- Model predictive control
Assessment
Assessment task 1: Quiz 1
| Intent: | The aim of this quiz is to assess the competence in image processing techniques. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 2 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 5% |
Assessment task 2: Quiz 2
| Intent: | The aim of this quiz is to assess the competence in sensors, sensor processing and control techniques. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 15% |
Assessment task 3: Project
| Intent: | For students to apply sensing and control strategies to achieve an integrated solution to given specifications in a robotics system specifications. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 50% |
Assessment task 4: Timed Take Home Exam
| Intent: | The aim of the final exam is to assess students' deeper knowledge gained through lectures, lab classes and projects. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 30% |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
References
[1] Nixon, Mark S, Feature extraction & image processing for computer vision, Oxford : Academic, 2012
[2] Introduction to Sensors for Ranging and Imaging by Graham Brooker, Sci Tech Publishing Inc., 2009
[3] Image Processing Toolbox, https://au.mathworks.com/products/image.html
[4] Image Acquisition Toolbox, https://au.mathworks.com/products/imaq.html
[5] Camera Calibration Toolbox, http://www.vision.caltech.edu/bouguetj/calib_doc/
[6] Katsuhiko Ogata, Modern Control Engineering, (3rd Edition)
[7] Jefferey B. Burl, Linear Optimal Control, Addison Wesley.
[8] Control Tutorial for MATLAB, http://www.engin.umich.edu/group/ctm/state/state.html
[9] J.M. MacIejowski, Multivariable Feedback Design, Addison-Wesley, 1989
[10] Branislav Kisacanin, Gyan C. Agarwal, Linear Control Systems: With Solved Problems and MATLAB Examples
[11] Control system toolbox, http://www.mathworks.com/products/control/
[12] Katsuhiko Ogata, Discrete-Time Control Systems (2nd Edition)