Master of Science
MSc Advanced Control and Systems Engineering
A world-leading education in the theoretical developments and practical applications of modern control and systems engineering.
Due to high demand for this course, we operate a staged admissions process with multiple selection deadlines throughout the year, to maintain a fair and transparent approach.
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Fees and funding
Fees
Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:
-
MSc (full-time)
UK students (per annum): £14,700
International, including EU, students (per annum): £38,400
The fees quoted above are fully inclusive of tuition, administration and computational costs.
Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .
Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.
International student CAS deposit
Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
We offer a number of postgraduate taught scholarships and awards to outstanding UK and international students each year.
The University of Manchester is committed to widening participation in master's study, and allocates £300,000 in funding each year. Our Manchester Master's Bursaries are aimed at widening access to master's courses by removing barriers to postgraduate education for students from underrepresented groups.
We also welcome the best and brightest international students each year and reward excellence with a number of merit-based scholarships. See our range of master’s scholarships for international students .
And, if you have completed an undergraduate degree at The University of Manchester, or are currently in your final year of an undergraduate degree with us, you may be eligible for a discount of 10% on tuition fees if you choose to study on a taught postgraduate course here. Find out if you're eligible and how to apply .
For more information on master's tuition fees and studying costs, visit the University of Manchester funding for master's courses website to help you plan your finances.
Course unit details:
Digital Control and Model Predictive Control
| Unit code | EEEN60241 |
|---|---|
| Credit rating | 15 |
| Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
| Teaching period(s) | Semester 1 |
| Available as a free choice unit? | No |
Overview
The unit covers the following topics:
Part A - Digital Control
- Motivation for digital control theory, including computer-based control.
- Discrete representation of continuous systems: discrete transfer functions, the z transform, and state-space modelling.
- Stability analysis.
- Control system design concerns in practice (e.g. sampling rate selection, computational time) in the discrete domain.
- Classical anaysis in the discrete domain.
Part B - Model Predictive Control (MPC)
- MPC control formulation: simple unconstrained optimal control formulation, general characteristics of MPC formulation, translation of MPC problem into quadratic programming optimisation problem, constant output disturbance observer model, infeasibility and softening of the constraints.
- Practical MPC implementation considerations: empirical model development, usage of design/tuning parameters, implementation/commissioning of MPC.
- Design of MPC control for typical CSTR chemical reactor as well as distillation column.
Aims
The unit aims to introduce students to the fundamental concepts and their applications of digital control, and the formulation and the main implementation details redarding Model Predictive Control (MPC) as well as the real-time process optimisation.
Learning outcomes
On successful completion of the course, a student will be able to:
ILO 1: Recognise the relevance of discrete-time models for practical control.
ILO 2: Relate classical control to digital control systems. [Developed and Assessed].
ILO 3: Analyse digital control systems using transfer function and state space modelling techniques. [Developed and Assessed].
ILO 4: Describe Model Predictive Control problem formulation using state-space system model format. [Developed and Assessed].
ILO 5: Convert optimisation-based control problem formulation into general mathematical programming formulation. [Developed and Assessed].
ILO 6: Derive and analyse unconstrained optimal control law for simple low-order single-input, single-output systems. [Developed and Assessed].
ILO 7: Design Model Predictive Control by selecting appropriate weights in the corresponding cost function. [Developed and Assessed].
ILO 8: Summarise the key steps of implementing Model Predictive Control, including development of empirical prediction model and the procedure of controller conditioning. [Developed and Assessed].
Syllabus
The unit covers the following topics:
Part A - Digital Control
- Motivation for digital control theory, including computer-based control.
- Discrete representation of continuous systems: discrete transfer functions, the z transform, and state-space modelling.
- Stability analysis.
- Control system design concerns in practice (e.g. sampling rate selection, computational time) in the discrete domain.
- Classical anaysis in the discrete domain.
Part B - Model Predictive Control (MPC)
- MPC control formulation: simple unconstrained optimal control formulation, general characteristics of MPC formulation, translation of MPC problem into quadratic programming optimisation problem, constant output disturbance observer model, infeasibility and softening of the constraints.
- Practical MPC implementation considerations: empirical model development, usage of design/tuning parameters, implementation/commissioning of MPC.
- Design of MPC control for typical CSTR chemical reactor as well as distillation column.
Teaching and learning methods
Lectures and lab sessions.
Assessment methods
| Method | Weight |
|---|---|
| Written exam | 80% |
| Report | 10% |
| Practical skills assessment | 10% |
Feedback methods
Exam - 3 hours, 4 questions. Standard feedback is provided after the exam board.
Digital Control - lab. Individual feedback is provided 3 weeks after submission.
MPC Control of Simulated Distillation Column - lab. Online submission, individual performance feedback is provided two weeks after students submit their answers.
Recommended reading
Franklin, G. F., Powell, J. D., & Workman, M. L. (1998). Digital Control of Dynamic Systems (3rd ed.). Addison Wesley.
Åström, K. J., & Wittenmark, B. (Eds.). (1984). Computer Controlled Systems: Theory and Design. Prentice-Hall.
Maciejowski, J. M. (2001). Predictive Control with Constraints. Prentice Hall
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 42 |
| Practical classes & workshops | 6 |
| Tutorials | 6 |
| Independent study hours | |
|---|---|
| Independent study | 96 |
Teaching staff
| Staff member | Role |
|---|---|
| Ognjen Marjanovic | Unit coordinator |
| Guang Li | Unit coordinator |
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