MSc Advanced Control and Systems Engineering / Course details

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.

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.

ILO1 Describe the implementation of the data-driven techniques that can be used to identify linear and nonlinear dynamic systems. [Developed and Assessed].

ILO2 Use statistical analysis techniques to describe random variation in measured data. [Developed and Assessed].

ILO3 Demonstrate understanding of the potential and limitations of AI and thier essential steps for dynamic system modelling, including choices of AI models and performance evaluation. [Developed and Assessed].

ILO4 Apply the neural networks models for constructing dynamic models. [Developed and Assessed].

ILO5 Optimise the neural networks model parameters with input-output data. [Developed and Assessed].

ILO6 Test and validate the neural networks models with different selecting criteria.

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.

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