MEng Mechatronic Engineering / Course details

Year of entry: 2024

Course unit details:
Digital Control & System Identification

Course unit fact file
Unit code EEEN44411
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

BRIEF DESCRIPTION OF THE UNIT:

The unit has two different parts:

Part A. Digital Control

1.     Motivation for digital control theory, including computer-based control.

2.     Discrete representation of continuous systems: discrete transfer functions and an overview of state space descriptions

3.     Stability analysis.

4.     Control system design (including PID structure and others) in the discrete domain.

5.     Classical analysis in the discrete domain.

6.     Control design for mobile robots in the discrete domain.

Part B. System Identification

1.     Exemplar system identification problems

2.     Measurements and Statistics.

3.     Non-parametric Methods: Time and frequency domain.

4.     Least square problem Statistic foundation.

5.     Parametric methods (ARX, OE).

6.     Input design.

7.     Optimization: gradient method for OE.

8.     Recursive estimation.

9.     Validation.

 

Pre/co-requisites

Unit title Unit code Requirement type Description
Control Systems I EEEN20252 Pre-Requisite Compulsory

Aims

This course unit detail provides the framework for delivery in the current academic year and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates

The course unit aims to:

  • Show how discrete-time transfer functions can be used to model dynamic systems with sampling and modulation;
  • Control systems analysis and synthesis in the discrete domain.
  • Relations between discrete-time and continuous-time models;
  • How system identification algorithms can be used to find models of dynamic systems.
  • How least squares approaches can be used for parameter estimation.
  • The influence of noise on the parameter estimation;
  • The relevance of measurement theory for the identification process.

 

 

Learning outcomes

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

Developed

Assessed

ILO 1

Demonstrate understanding of techniques for identifying dynamic systems

X

X

ILO 2

Recognise the relevance of discrete-time models for practical control.

X

X

ILO 3

Demonstrate understanding statistical concepts applied in measurement theory.

X

X

ILO 4

Analyse digital control systems using transfer function techniques.

X

X

ILO 5

Relate classical control to digital control systems

X

X

ILO 6

Apply least squares and gradient descent optimization algorithms in the context of System Identification in MATLAB

X

X

ILO 7

Design and implement digital controllers

X

X

 

Teaching and learning methods

The unit is taught during a six-week period during which several 2-hour lectures take place every week and two 2-hour lab sessions are scheduled to take place during the 6-week period.

 

Assessment methods

Method Weight
Other 20%
Written exam 80%

Coursework Assessment task

How and when feedback is provided

Weighting within unit (if relevant)

 

System Identification coursework

Individual feedback is provided 3 weeks after submission

10%

Digital control coursework

Individual feedback is provided 3 weeks after submission

10%

 

Feedback methods

.      

Recommended reading

1.         Söderström T. System Identification . (Stoica P, ed.). New York¿;: Prentice Hall; 1989.

2.         Ljung L. System Identification¿: Theory for the User . Englewood Cliffs¿;: Prentice-Hall; 1987.

3.         Goodwin GC (Graham C. Dynamic System Identification¿: Experiment Design and Data Analysis . (Payne RL, ed.). New York: Academic Press; 1977. OVP.

4.        Franklin, Powell and Workman, "Digital Control of Dynamic Systems", Addison Wesley, 1998, 3rd Edition.

5.   Åström KJ (Karl J. Computer Controlled Systems¿: Theory and Design . (Wittenmark B, ed.). Englewood Cliffs¿;: Prentice-Hall; 1984.

Study hours

Scheduled activity hours
Lectures 24
Practical classes & workshops 4
Independent study hours
Independent study 122

Teaching staff

Staff member Role
Ognjen Marjanovic Unit coordinator
Guang Li Unit coordinator

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