Module overview
This module aims to introduce to the students advanced model based signal processing methods and systems design theories, with illustrative case studies to demonstrate how the knowledge obtained in this module can be used in some challenging real life applications.
The module uses the specialist computation/simulation tool Matlab.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Design and implement model based control systems
- Evaluate the performance of a stochastic system using Monte Carlo methods
- Apply the model based signal processing and system design methods to real life applications
- Estimate unknown system parameters from noisy measurement data
- Estimate system state information from noisy measurements
Syllabus
The course will cover the following topics:
Review of mathematical background
- Review of state space modelling
- Review of linear algebra
- Review of probability
Stochastic simulation and Monte Carlo method
- Random Number Generation
- Monte Carlo method
Stochastic simulation using Monte Carlo simulation
- Stochastic signal processing, focusing on
- Estimation problem and least squares
- Kalman filtering and Extended Kalman filtering
- Particle Filtering
Advanced system control theory
- Optimal Control: LQR and LQG
- Receding horizon methods
A case study: next generation health care – electrical stimulation and robotic-assisted upper-limb stroke rehabilitation.
Learning and Teaching
| Type | Hours |
|---|---|
| Lecture | 36 |
| Wider reading or practice | 29 |
| Tutorial | 12 |
| Follow-up work | 18 |
| Preparation for scheduled sessions | 18 |
| Completion of assessment task | 37 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Graham C. Goodwin, Stefan F. Graebe and Mario E. Salgado (2001). Control System Design. Prentice Hall.
James V. Candy (2005). Model-Based Signal Processing. Wiley-IEEE Press.
Dan Simon (2006). Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. John Wiley & Sons.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 30% |
| Coursework | 20% |
| Coursework | 20% |
| Coursework | 10% |
| Coursework | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External