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MSc Advanced Control and Systems Engineering with Extended Research / Course details

Year of entry: 2020

Course unit details:
Digital Control & System Identification

Unit code EEEN60110
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Department of Electrical & Electronic Engineering
Available as a free choice unit? No

Overview

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 EEEN20030 Pre-Requisite Compulsory
Control Fundamentals EEEN60108 Pre-Requisite Compulsory

Aims

The course unit aims to give students an understanding of:

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. Application to real mobile robots;

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 to identification.

Learning outcomes

Students will be able to:

Knowledge and understanding

  • Demonstrate understanding of techniques for identifying dynamic systems and knowledge of methods for implementing parameters estimation algorithms.
  • Understand the relevance of discrete-time models for practical control.
  • Demonstrate understanding statistical concepts applied in measurement theory.
  • Demonstrate understanding of discrete-time control techniques for mobile robots

Intellectual skills

  • Dynamic, statistical data analysis.
  • Optimization approaches for parameter estimation.
  • Relate classical control concepts to digital control systems.
  • Analyse digital control systems using transfer function and state space techniques.

Practical skills

  • Be able to analyse, model and validate data sets containing dynamic relationships.
  • Understand how to be able to use MATLAB, and relevant toolboxes, for system identification.
  • Design and implement digital controllers
  • Design and implement digital controllers for real mobile robots
  • Be able to build a real robot

Transferable skills and personal qualities

  • Dynamic, statistical data analysis.
  • Fundamental optimization concepts.
  • Understand the relation between computer-aided-design and practical implementation.

Teaching and learning methods

Number of Hours Allocated to:

Lectures

Tutorials

Practical Work/ Laboratory

Private Study

Total

30

6

0

114

150

 

Assessment methods

Method Weight
Other 20%
Written exam 80%

Written Examination

Four questions, answer all questions

Length of examination: 3 hours

Calculators are permitted

The examination forms 80% of the total unit assessment

Course Work Not Based On Laboratory Attendance

Assignment 1: Design Assignment with report

Submission date: Sunday, Week 5, Semester 1

The assignment forms 10% of the total unit assessment

Assignment 2: Design Assignment with report

Submission date: Sunday, Week 6, Semester1

The assignment forms 10% of the total unit assessment

Study hours

Scheduled activity hours
Lectures 30
Practical classes & workshops 9
Tutorials 6
Independent study hours
Independent study 105

Teaching staff

Staff member Role
Joaquin Carrasco Gomez Unit coordinator
Alexandru Stancu Unit coordinator

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