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Autonomous and Intelligent Systems
Department of Automatic Control and Systems Engineering,
Faculty of Engineering
As the need and scope for robots and machine intelligence in all aspects of society grows, so does the market for those trained in automation and systems engineering, especially those with a background in Autonomous and Intelligent Systems.
The course covers all major aspects of control and systems engineering with an emphasis on system autonomy and intelligence. Autonomous systems is a fast changing area and has applications in a range of modern engineering disciplines including computer science, mechanical engineering, electrical and electronic engineering and materials science engineering.
You’ll develop an extensive understanding of the field of autonomous and intelligent systems, studying subjects such as artificial intelligence, autonomous agents, evolving intelligent systems and intelligent control theory.
Through lab work, you will get hands-on experience of working with various types of autonomous and intelligent systems, and have the opportunity to build a system from scratch using the specialist labs in the University.
You’ll also work on a research-level dissertation project of your own. You’ll be able to pick an area you are passionate about and develop a solution to a real world problem in the area. You may choose from a range of project ideas, including bionic systems, manufacturing lines, autonomous vehicles or drones. Your project supervisor - an academic dedicated to helping you with your project - will support you.
- Agent-Based Modelling and Multi-Agent Systems
This module introduces multi-agent systems and agent-based modelling. It will motivate the many diverse types of complex adaptive system that can be represented and simulated by these approaches (including engineering applications, artificial intelligence and socio-technical systems). The module introduces a set of key theories, methods and tools in the domain of multi-agent systems and agent-based models. You will gain experience of implementing agent-based simulations in state-of-the-art software, building on your experience in object-oriented programming and agile software development methods.15 credits
- Real-Time Embedded Systems
Many systems, for example; a control system, fault detection system or health monitoring system are required to work in real-time. Such systems can be developed and implemented using a CPU and external devices in an embedded system application/device to perform the desired tasks in the “real” world. This module covers the hardware associated with building an embedded system and how the desired functionality and thus real-time operation of an embedded system can be realised through software/hardware.15 credits
- Modern Control & System Identification
This module introduces you to advanced state-space control systems analysis and design methods for multivariable systems. The focus is linear time-invariant (LTI) systems in the continuous-time domain, although an introduction is also provided to discrete-time cases and nonlinear cases. You will also be introduced to system identification techniques. System identification uses observations of inputs and outputs from physical systems and estimates dynamical models directly. The theoretical framework and the computational algorithms are explored using synthetic and real problems to show how models can be estimated and validated for future use.15 credits
- Data Modelling and Machine Intelligence
All of our lives are affected by machine intelligence and data models - Google is a very visible example; but if you are a victim of identity theft, if you want a loan to buy a house or if you want to pass through immigration at an airport, a model derived from data using some form of machine learning technique will be involved. Engineers increasingly look to machine intelligence techniques such as neural networks and other machine learning methods to solve problems that are not amenable to conventional analysis e.g. by application of Newton's & Kirchhoff's laws, and other physical principles. Instead, they use measurements of system variables to compute a model of the process that can then be used in design, analysis and forecasting. System identification is a specific example of data modelling. We will look at the underlying principles of machine learning, the advantages and limitations of the various approaches and effective ways of applying them with the aim of making you a competent practitioner.15 credits
- Deep Learning
An important field within artificial intelligence is machine learning, which enables systems to learn from data rather than being explicitly programmed to solve a task. Conventional machine learning algorithms tend to rely on a human to carefully engineer and extract features to present to a machine learning algorithm, which can be time-consuming and difficult. A deep learning system, by contrast, takes raw data as input and learns to extract features automatically. This approach has led to significant improvements in processing images, video, speech and audio. Deep learning has also had an impact on the design of intelligent agents, giving rise to the area of deep reinforcement learning, which is where an agent learns in a reward-based framework. An example of deep reinforcement learning is where the Google DeepMind team designed an agent that learned to play Atari computer games to better-than-human-expert level.15 credits
- Multisensor and Decision Systems
The ability to use data and information from multiple sources and make informed decisions based on that data is key to many applications, e.g. manufacturing, aerospace, robotics, finance and healthcare. Through effective use of multisensory data and decision making we can reduce uncertainty, improve robustness and reliability, enhance efficiency and ultimately improve the performance of systems. In this module you will develop an in depth knowledge and understanding of multisensor and decision systems and the underlying mathematics and algorithms. You will develop your confidence in solving complex problems requiring the application of multisensory and decision techniques to a wide variety of applications.15 credits
- Control Systems Project and Dissertation
The aim of the project is to give you the opportunity to develop further your advanced knowledge and skills and apply these to a specific problem or set of problems. It builds on the taught modules and develops a greater level of independence. You will be allocated a project supervisor with whom you will develop the project specification and who will provide overall guidance on the project. However, you are expected to demonstrate a high level of initiative and independence. You will also develop skills in creative and critical thinking, analysis, reflection, effective project management and communication. The project is very different from many of your taught modules where the lecturer takes the lead in your learning. In the project you are expected to take the lead and the supervisor is expected to provide overall guidance and help.60 credits
Optional modules - examples include:
- Industrial Training Programme (ITP) in Computational Intelligence
This module will provide an insight into advanced computational intelligence systems via industry-relevant project work. This will be in collaboration with an industrial partner. The industrial partner will set a real technical challenge and your group will undertake practical and theoretical work and present a report that will also require an in-depth literature review. To supplement the main technical challenge there will be focused technical seminars on relevant topics. These topics will be provided by both academics and industry engineers. In addition, the industrial partner will provide seminars relevant to both professional and technical skills to help you complete the project.15 credits
- Advanced Control
The aim of this module is to provide you with an introduction to some of the advanced control techniques used in modern control engineering research and industrial applications. The module will cover both theory and practice, involving analysis and design.15 credits
Different control techniques and applications may be covered in different years. In all cases, the basic principles and concepts of a particular control technique will be introduced, and comparisons and contrasts will be made with other techniques. Subsequently, the design, analysis and implementation of advanced controllers or control laws will be covered, starting from the requirements of the basic control problem for the application at hand (i.e. stability in the presence of constraints; disturbance and noise rejection). Controller design will be illustrated by industrially-relevant case studies.
- Mobile Robotics and Autonomous Systems
Robotics and autonomous systems are having an increasing impact on society and the way we live. From advanced manufacturing and surgical robots to unmanned aerial systems and driverless cars, this exciting area is presenting increasing technological challenges. This module provides you with the advanced knowledge and understanding to apply control and systems engineering concepts to the closely related disciplines of robotics and autonomous systems. The module covers theoretical and technical analysis, and design aspects of mobile and manipulator robots with reference to their applications. The module further covers advanced techniques in autonomous decision making for robots and autonomous vehicles.15 credits
- Machine Vision
The module gives knowledge of machine vision methods for a broad range of applications. It introduces you to image and video processing models and methods and provides you with skills on how to embed them in autonomous systems. You will be able to apply the acquired knowledge to both industrial and research areas.15 credits
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption. We are no longer offering unrestricted module choice. If your course included unrestricted modules, your department will provide a list of modules from their own and other subject areas that you can choose from.
1 year full-time
There are lectures, seminars, tutorials, individual assignments and a major research project.
You’ll be assessed via exams, coursework assignments and a project dissertation.
Our courses are informed by our strong links with industry and our research. This MSc is an ideal preparation for students aiming to move onto a PhD and research career, or those who want to work in industry with autonomous and intelligent systems. The MSc covers the required technical knowledge and skills, along with wider professional skills such as critical thinking, project management and communication, for graduates to excel in their chosen career.
A 2:1 honours degree (or equivalent) in control engineering or a related subject.
Alternatively, you might be an experienced professional, thinking about updating your knowledge of the subject.
Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
If you have any questions about entry requirements, please contact the department.
Fees and funding
You can apply for postgraduate study using our Postgraduate Online Application Form. It's a quick and easy process.
+44 114 222 5644
Any supervisors and research areas listed are indicative and may change before the start of the course.
Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.