Master of Engineering (MEng)

MEng Mechatronic Engineering

*This course is now closed for applications for 2025 entry.

  • Duration: 4 years
  • Year of entry: 2025
  • UCAS course code: HHH6 / Institution code: M20
  • Key features:
  • Scholarships available
  • Accredited course

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Fees and funding

Fees

Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £34,000 per annum. For general information please see the undergraduate finance pages.

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

The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.

For information about scholarships and bursaries please visit our undergraduate student finance pages and our Department funding pages .

Course unit details:
System Identification and Artificial Intelligence

Course unit fact file
Unit code EEEN40231
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

The unit has two different parts.

Part A - System Identification

Examplar system identification problems.
Measurements and statistics.
Non-parametric methods: Time and frequency domain
Least square problem. Statistic foundation.
Parametric methods (ARX, OE).
Input design.
Optimisation: gradient method for OE.
Recursive estimation.
Validation.

Part B - Artificial Intelligence (for dynamic systems modelling)

Introduction to AI.
Neural network models (single-layer and multiple-layer neural networks).
Deep learning (neural networks).
Learning and optimisation methods.
Performance evaluation.

Pre/co-requisites

Unit title Unit code Requirement type Description
Control Systems II EEEN30231 Pre-Requisite Compulsory
To select EEEN40231 you need to have taken unit EEEN30231 Control Systems II in your 3rd year.

Aims

The unit aims to give students an understanding of how system identification and artificial intelligent algorithms can be used to find models of dynamic systems; how least squares and optimisation approaches can be used for parameter estimation; the influence of noise on the parameter estimation and the relevance of measurement theory for the identification process.

Learning outcomes

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

ILO 1: Test and validate the neural networks models with different selecting criteria.

ILO 2: Optimise the neural networks model parameters with input-output data.

ILO 3: Apply least squares and gradient descent optimization algorithms in the context of System Identification in MATLAB.

ILO 4: Demonstrate understanding statistical concepts applied in measurement theory.

ILO 5: Demonstrate understanding of techniques for identifying dynamic systems.

ILO 6: Apply the neural networks models for constructing dynamic models.

ILO 7: Demonstrate the understanding AI and their essential steps for dynamic models.

Teaching and learning methods

Lectures, tutorial and laboratory.

Assessment methods

Method Weight
Written exam 80%
Report 20%

Feedback methods

Examination - feedback will be given after the exam board.

System Identifaction coursework - individual feedback is provided 3 weeks after submission.

AI coursework - individual feedback is provided 3 weeks after submission.

Recommended reading

Söderström, T. (1989). System Identification. Prentice Hall.
Ljung, L. (1987). System Identification: Theory for the User. Prentice-Hall.
Goodwin, G.C., & Payne, R.L. (Ed.). (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Alpaydin, E. (2020). Introduction to Machine Learning (4th ed.). MIT Press

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
Long Zhang Unit coordinator

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