Module overview
This module introduces the idea of signal analysis, and the mathematical concepts and methods used to classify, transform, and analyse signals. These fundamentals are then applied within then context of control, which is intended to give the students skills an knowledge to apply in many control scenarios - for example PID controllers. The course provides a route into more complex control modules in later years, in terms of both the mathematical theory and the ideas.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Analyse linear systems using time and frequency domain methods
- Identify the advantages and problems arising from processing signals in quantised time and space
- Use the laplace transform to demonstrate stability of a transfer function
- Explain the difference between open loop and closed loop control
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Identify the real-world implications of stability and controllability
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Model a real-world system as a transfer function
- Use fourier analysis to design a filter
- Classify real-world data into different types of signals
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Use discrete PID controllers to control a real-world system
- Implement signal processing using digital filters
Syllabus
Signals theory:
-Classification of signals
-Mathematical systems
-Linear systems
-Classical filters (intro)
-Fourier transform
Digital signals:
-Sampling (Nyquist) and ADCs.
-Signal encoding
-Discrete fourier methods
Control theory:
- Feedback and open- vs closed-loop
- Stability, controllability
- The laplace transform
- Importance of impulse responses
Controller design:
- Modelling real systems as transfer functions
- PID controllers
- Discrete-time controllers
Learning and Teaching
Teaching and learning methods
- Lectures
- Guided self-study
- Laboratories in computing and specialist hardware
| Type | Hours |
|---|---|
| Specialist Laboratory | 20 |
| Lecture | 32 |
| Preparation for scheduled sessions | 20 |
| Independent Study | 78 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Exam | 50% |
| Class Test | 10% |
| Computing Laboratories | 20% |
| Specialist Laboratory | 20% |