MSc Advanced Control and Systems Engineering

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 .

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).

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.

BRIEF DESCRIPTION OF THE UNIT

• Quadratic Lyapunov functions for linear systems

• LQR (optimal state feedback) control in both Continuous-Time and Discrete-Time

• Robustness of LQR control in both Continuous-Time and Discrete-Time

• Kalman filter (optimal observers) in both Continuous-Time and Discrete-Time

• Linear Quadratic Gaussian (LQG) control (combining LQR state feedback and optimal observer) in both Continuous-Time and Discrete-Time

• Loop transfer recovery

• Adding integral action

• H2 norms and H2 optimal control

• Connection of LQG control and MPC (Model Predictive Control)

• Practical considerations in optimal control and estimation

The unit delves into the principles of optimal control and estimation, addressing both theoretical foundations and the practical challenges associated with implementing these techniques in real-world scenarios. It covers key topics such as dynamic programming, linear quadratic regulators (LQR), linear quadratic Gaussian (LQG) methods, and Kalman filtering, ensuring a robust understanding of the underlying mathematics and algorithms. A significant emphasis is placed on the discrete-time implementation of these methods, exploring how they can be effectively applied in digital systems. Additionally, the unit integrates practical case studies across various engineering applications, providing insights into the limitations, trade-offs, and adaptations required for successful deployment in diverse contexts, such as robotics, aerospace, and industrial automation. Through hands-on exercises and problem-solving, students will gain both theoretical knowledge and practical experience, preparing them for tackling complex control and estimation challenges in modern engineering systems.

The course unit aims to:

• Introduce students to the fundamentals of LQR and KF

• Introduce students to the fundamentals of LQG control

• Introduce students to the fundamentals of robustness analysis, robust control law synthesis and robust control design

On successful completion of the course, a student will be able to:

ILO 1 Demonstrate a comprehensive understanding of optimal control theory.

ILO 2 Explain the process of synthesising optimal controllers. 

ILO 3 Design optimal controllers for dynamic systems.

ILO 4 Apply optimal control theory to the design of controllers.

ILO 5 Develop strategies for controlling systems in scenarios where accurate mathematical models are unavailable.

ILO 6 Implement optimal control methods in systems across various technological domains. 

ILO 7 Employ optimal estimation techniques in a range of practical applications. 

ILO 8 Utilise design methodologies for developing controllers in real-world systems.

ILO 9 Apply Kalman filtering techniques to fields beyond control engineering.

ILO 10 Adapt and utilise the learned methods effectively in diverse applications.

Theoretical knowledge is delivered over lectures and demonstrated over tutorial.

Unseen written examination - 3 hours - weighting 80%

Coursework - 8 hours - weighting 20%

Written Exam

Feedback is provided after exam board.

Coursework Assignments

Individual feedback is provided 3 weeks after submission.   

1.Applied optimal control: optimization, estimation and control. Bryson, Arthur Earl. Routledge, 2018. 

2. Optimal Control. Lewis, Frank L. John Wiley & Sons 2012 

3. Multivariable feedback control : analysis and design. Skogestad, Sigurd. John Wiley, 2005 

4. Linear optimal control Anderson, Brian D. O. Prentice-Hall, 1971