Applied AI and Control
| Module title | Applied AI and Control |
|---|---|
| Module code | EMG3006 |
| Academic year | 2025/6 |
| Credits | 15 |
| Module staff | Dr Tim Hughes (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 25 |
|---|
Module description
This module explores the artificial intelligence paradigm and its capacity for developing smarter automation and control systems. Through practical examples, and underpinned by rigorous theory, you will develop an understanding of the key objectives of intelligent agents, namely to correctly interpret data from observations of their environment, to learn from data, and to carry out tasks and complete goals by responding to the learning. Key to this process are feedback loops between the agent and its environment. Control theory is the science of feedback mechanisms and as such underpins the AI paradigm.
Many current developments in the field of artificial intelligence combine data analysis, machine learning and control engineering approaches and so enable intelligent agents to complete complex tasks. In this module you will develop skills in signal processing, reinforcement/adaptive learning, dynamical systems and control, and apply these to design intelligent agents in relevant application areas such as autonomous vehicles, communication and information systems, policy making for sustainability, infectious disease control, and smart grid technologies.
Module aims - intentions of the module
In this module you will develop expertise in modern mathematical and computational tools from control theory and the artificial intelligence paradigm. You will develop a general perspective on controller design for optimal and robust control problems and an understanding of modern methods of intelligent designs based on adaptive control or reinforcement learning methodologies. You will study specific examples of optimal control, for example L(inear) Q(uadratic) G(aussian) approaches; and of robust control, for example H-infinity methods. You will gain hands-on experience of computational implementation of these control schemes and develop an appreciation of issues such as robustness and computational complexity.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Formulate and solve a range of control problems, and understand the potential and limitations of AI for smart automation and management;
- 2. Use relevant computational tools to find exact or approximate solutions;
- 3. Understand aspects of stability, optimality and robustness in control systems design;
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Communicate the importance of optimality and robustness in management and control, and the promise and limitations of model-free learning-based approaches;
- 5. Use a range of appropriate computational platforms/software;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 6. Communicate the value of optimisation and control to stakeholders in the energy and environmental sciences sectors;
- 7. Effective use of learning resources;
- 8. Report writing and presentation.
Syllabus plan
The module will be structured in blocks in which a specific control or AI methodology is introduced then applied within project-based work. The specific topics may vary over time to reflect the most up to date research and educational practice. Examples of the material to be covered include:
Optimal control (e.g., Pontryagin’s maximum principle, Hamilton-Jacobi-Bellman equations, L(inear) Q(uadratic) G(aussian) control, Model Predictive control);
Robust control (e.g., H-infinity methods, passivity based control);
Adaptive and learning control (e.g., lambda-tracking, reinforcement learning);
Data-driven control and optimization (e.g. artificial neural networks, evolutionary algorithms, biomimicry).
Theory and methodologies will be illustrated with practical applications, such as the design of driverless transport (e.g., unmanned aerial and underwater vehicles for remote sensing); energy systems and the smart grid; sustainable economic policy making; and infectious disease control. This will be complemented by hands-on demonstrations based on LEGO Mindstorms self-balancing robots or mini-drones or remote sensing and Internet of Things technologies.
The assessment structure on this module is subject to review and may change before the start of the new academic year. Any changes will be clearly communicated to you before the start of term and if you wish to change module as a result of this you can do so in the module change window.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 33 | 117 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 9 | Lectures and tutorials |
| Scheduled Learning and Teaching Activities | 24 | Practicals and supervised project work |
| Guided Independent Study | 67 | Self-study and background reading |
| Guided Independent Study | 50 | Report writing and preparation for presentations |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Informal exercises and practicals | One per topic | 1-6 | Oral within scheduled sessions and office hours |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Worksheets | 60 | One per topic | 1-5, 7 | Written and Oral |
| Extended investigation | 40 | To be submitted in one or two formats (report, poster, presentation or other digital media) to demonstrate skills in scientific communication in addition to topic mastery. | 1-8 | Written and Oral |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Worksheets | Coursework | 1-5, 7 | To be agreed by consequences of failure meeting |
| Extended investigation | Coursework | 1-8 | To be agreed by consequences of failure meeting |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to resubmit the original assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 40%.
Indicative learning resources - Basic reading
Basic reading:
- Khalil, H.K., Nonlinear Systems, Prentice-Hall, 2000, ISBN: 000-0-132-28024-8.
- Sontag, E.D., Mathematical Control Theory, Springer, 1998, ISBN: 987-0387984895.
- Kirk, D.E., Optimal Control Theory: An Introduction, Dover, 2004, ISBN: 978-0486434841.
- Rogers, S. and Girolami, M., A first course in machine learning, CRC Press, 2016.
- Steven L. Brunton, S.L. and Kutz, J.N., Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, 2019, ISBN: 978-1108422093.
- Ertel, W., Introduction to artificial intelligence, 2ed, Springer, 2018, ISBN: 978-3319584867.
- Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ed, Pearson, 2016, ISBN: 978-1292153964.
Web-based and electronic resources:
- ELE – College to provide hyperlink to appropriate pages
Other resources:
N.A.
| Credit value | 15 |
|---|---|
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 6 |
| Available as distance learning? | No |
| Origin date | 01/05/2025 |
