MEng Mechatronic Engineering with Industrial Experience

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.

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.

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.

Lectures, tutorial and laboratory.

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.

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

Tuition fees for home students commencing their studies in September 2027 will be £10,050 per annum. Tuition fees for international students will be £37,500 per annum.

For general information please see the undergraduate finance pages.

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).

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