| Effective Term: | 2025/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Advanced Control System Engineering |
| Unit ID: | ENGRG9203 |
| Credit Points: | 15.00 |
| Prerequisite(s): | (ENGRG4201) |
| Co-requisite(s): | Nil |
| Exclusion(s): | (ENGIN5405) |
| ASCED: | 031301 |
| Other Change: | |
| Brief description of the Unit |
| This is a capstone unit enhancing students knowledge and skills in the advanced topics of control system engineering. Through this unit students will appraise their understanding of the applications and importance of control system in mechatronic applications. Students will be able to interpret, analyse and exemplify different areas of automated control systems, digital control systems, predictive control systems and real time realisation using suitable control system software. Integrating this unit with the knowledge and understanding obtained in previous units, students will be able to distinguish between the principles of different control systems and will be able to apply them in developing and designing appropriate engineering processes. The theoretical knowledge will be complemented with projects and laboratory exercises. The activities will allow students to enhance their skills in designing software based models for controlling mechatronic systems. The students will also learn to appreciate applications of the developed knowledge and skills in industrial environment. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: No |
| Supplementary Assessment: |
| Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | | | | | | Intermediate | | | | | | | | Advanced | | | |  | | |
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| Learning Outcomes: |
| On successful completion of the unit the students are expected to be able to: |
| Knowledge: |
| K1. | Demonstrate advanced understanding of the theory and applications of control systems and controllers. |
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| K2. | Analyse and interpret principles of digital control system. |
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| K3. | Demonstrate understanding of performing stability analysis of control systems. |
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| K4. | Explain and appraise different transformation techniques and methodologies. |
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| K5. | Demonstrate understanding of different control systems design. |
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| Skills: |
| S1. | Design, evaluate and critically analyse different control systems for stability and performance to ensure relevant criteria are met. |
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| S2. | Transform and evaluate different control systems. |
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| S3. | Design, analyse and perform real time realisation of different control systems using control software. |
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| Application of knowledge and skills: |
| A1. | Apply mathematical and theoretical knowledge to design and model an effective control system for a practical engineering process. |
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| A2. | Design and apply a suitable automatic control system in order to automate an industrial engineering process. |
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| Unit Content: |
Topics may include:
1. Review of control system engineering including stability and steady state errors.
2. Design via root locus, frequency response and state response.
3. Introduction to the advanced principles of digital control system.
4. Digital control system stability; the z-transform and stability analysis in z-domain.
5. Overview of tustin transform, w-transform and higher harmonic control.
6. Discrete system design and analysis.
7. Predictive control system: modelling and principles.
8. Optimal control: principles, designing and modelling.
9. Stochastic optimal control and nonlinear optimisation.
10. Advanced applications of calculus of variations to optimal control.
11. Computer / microprocessor based control, adaptive control and fuzzy logic control systems.
12. Multivariable controllers based on fuzzy logic and neural network methods.
13. Designing, modelling and real time realisation of different control systems using control software. |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 1. | S1-S3, A1-A2 | Experimental work and / or projects to verify students ability to apply knowledge and skills acquired in the unit. | Reports, demonstrations | 10-30% | | 2. | K1-K5, S1-S3 | Relevant tasks and problems to enforce understanding of the students and help in gradual development of knowledge and skills throughout the unit. | Assignments, quizzes | 10-30% | | 3. | K1-K5 | Questions and problems related to the unit contents. | Exams / Tests | 40-60% |
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