Advanced Control and Systems Engineering
Department of Automatic Control and Systems Engineering,
Faculty of Engineering
Accredited by the Engineering Council UK, Institution of Engineering and Technology and the Institute of Measurement and Control
Our flagship course blends theory and practice, giving you a strong grounding for a career in industry or research. This continually evolving course has been running for over 40 years and is well supported by the UK Engineering and Physical Sciences Research Council (EPSRC).
The core modules provide you with the basic skills you’ll need to become a control and systems engineer. You’ll take advanced modules in current areas of interest and complete a research-level dissertation project.
- Foundations of Control Systems
This is an introductory module on the foundations of control systems engineering. The aim of this module is to consolidate fundamental control and systems engineering aspects as well as introduce relevant topics to those new in the discipline. The module is separated in four (4) distinct themes: 1. introductory maths and MATLAB; 2. systems modelling and simulation; 3. control systems analysis and design; 4. digital control systems.30 credits
- Optimisation and Signal Processing
This module aims to provide detailed presentations to the use of the theory and methods of optimisation and signal processing for a wide range of engineering problems. In the optimisation part, in additional to traditional optimisation methods, topics based on recent developments in heuristic methods, such as evolutionary computing (e.g. swarm intelligence) will also be presented. While in the signal processing part, the concepts of sampling, digital filters and digital image processing will be introduced; the analysis methods of discrete signals and systems in both the time and frequency domain, and basic digital image processing methods will be delivered.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.
- Industrial Automation
Industrial automation has become an important feature today, especially in this age of rapid production and high15 credits
precision. Knowledge and skill in this area has therefore become increasingly necessary. This module aims to give you the opportunity to interact directly with devices such as sensors and actuators through Human Machine Interfaces (HMI) and PC; gather, process and monitor data locally or at remote locations; configure the hardware and program the latest version of Programmable Logic Controllers (PLC) used in industries. You will have the opportunity to learn PID controllers and their applications in industry.
- 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
- Control Systems Project and Dissertation
All of our lives are affected by 'machine intelligence' - 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. Fuzzy Logic based technology can be found in 'intelligent' household appliances as well as satellite systems and fuzzy logic control now provides a mainstream approach to control systems design.This module provides an introduction to the theory and practice of machine learning and data modelling, and to fuzzy logic within a control and systems engineering context. While related, the two approaches are different enough to be separated into two separate elements. Links between the two areas will be made where appropriate.We will look at the underlying principles of machine learning, data modelling and fuzzy logic, the advantages and limitations of the various approaches and effective ways of applying them in systems and control engineering, with the aim of making you a competent practitioner in the topic.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 b60 credits
Optional modules - examples include:
- Optimal Control
The module teaches how to design optimal controllers. It starts by explaining the main ideas of infinite-dimensional optimization. In particular, it introduces the Calculus of Variations, a field of mathematics used to find maxima/minima of mappings defined on functions e.g. it allows you to find a trajectory corresponding to the minimum energy/fuel waste. This theory is then used to develop tools for designing optimal controllers. Namely, the maximum principle and dynamic programming are introduced. The module is supported by extensive examples and home assignments that will help you to learn how to apply all the covered techniques.15 credits
- Industrial training programme (ITP) in Advanced Manufacturing
This module will provide an insight into advanced manufacturing 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
- Cybersecurity for control systems
The increase of sensing, computing, and communication technologies on control systems is enabling a host of new applications and services but it also opens the door to cybersecurity threats. Realizing the promise of secure control systems requires the development of analysis tools and design guidelines that integrate security guarantees in the performance characterization of the control system. This module aims to lay the theoretical foundations for secure control system design problems while explicitly teaching students how to account for the operational and practical constraints posed by real control systems.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
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.
You can expect a mix of lectures, tutorials, laboratory work and individual assignments.
You can use our award-winning take-home lab kits to explore core concepts at home. It supports our teaching, giving you the chance to learn by doing, when you want to, not just in classes. You’ll work on a major project of your own as part of your final assessment and there are chances to contribute to other projects throughout the course.
1 year full-time
Our courses are informed by our strong links with industry and our research. Graduates of this course go on to work as professional engineers in different sectors, including advanced manufacturing, software engineering, robotics and controls and automation systems engineering. Some graduates choose to follow up their studies with a research degree.
When I was researching where to study, I learned that the University of Sheffield’s Department of Automatic Control and Systems Engineering has an excellent reputation for research and links with industry, so it was an easy choice for me.
You’ll be an engineering, mathematics or science honours graduate with a 2:1 degree from a recognised institution. Or you may be an experienced professional, thinking about updating your knowledge of the subject. You’ll need to have excellent mathematical notation and basic computer programming skills.
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.