BEng (Hons) Electronics & Control Systems Engineering
Course Modules
ENRTA3065 - Digital Signal Processing
ENRTA3066 - Artificial Intelligence
ENRTA3020 - Control Systems Design
ENRTA3026 - Process Control
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Faculty of Technology and Environment
School of Engineering
Introduction to the course
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Diagram of this Course Modules and their credits
Your Programme Leader for this course in Electronics & Control Systems Engineering is Dr Mike Shaw (Email: M.M.Shaw@ljmu.ac.uk), please contact him if you require further information.
Dr Mike Shaw is Deputy Director of the School of Engineering and Faculty Head of International & Collaborative Programmes. He
has always had a professional involvement in Electronic Engineering, working for a number of years as a Radio & Electronics Officer with British Petroleum, and subsequently as an Engineer with Marconi Space & Defence Systems before joining what was then, Liverpool Polytechnic. Having joined the institution, initially on a part-time basis in 1986, Mike has had a number of jobs ranging from Senior Lecturer, Principal Lecturer and Director of the Centre for Precision Measurement and Industrial Inspection, and his current role as Deputy Director of the School of Engineering. During this time, he developed his research interests in high precision, optical non-contact measurement. He was latterly Faculty Head of Quality before moving to his current position as Faculty Head of International & Collaborative Programmes.Mike has always maintained a strong interest in his profession, particularly through the Institution of Electrical Engineers (IEE), or what is now known as the Institution of Engineering & Technology (IET), of which he is a Fellow, and as well as serving on the Council of the IEE, he was their Branch Chairman between 1999 - 2001.
Mike maintains his professional interests as director of the Rotary International project, The Excitement of Science. Run in conjunction with the Royal Institution in London, in the world famous Faraday Theatre, the project aims to raise the profile of science and engineering, by firing the imagination and enthusiasm, particularly of young people, in the way that only well conducted, participative and presented science can do.
ENRTA3065 - Digital Signal Processing
Module Leader
Dr Munther Gdeisat
Senior Lecturer in Digital Systems
General Engineering Research Institute
James Parsons Building
Email: m.a.gdeisat@ljmu.ac.uk
Dr. Munther Gdeisat is a Senior Lecturer in Digital Signal Processing at Liverpool John Moores University (LJMU). He received his BEng in Communications Engineering from Yarmouk University-Jordan in 1994. He was awarded his PhD in image processing from Liverpool John Moores University in 2000. To continue his research, the General Engineering Research Institute (GERI-LJMU) has appointed him as a postdoctoral research for two years. Dr. Gdeisat worked in Hertfordshire University as Senior Lecturer in Digital Systems for two years. Then he returned back to GERI to continue his research in image processing, fringe pattern analysis, phase unwrapping and wavelet transform. He published more than 15 papers in international journals conferences.
Introduction
This module builds on the level two module in maths, signal processing and simulation to provide an extensive knowledge of Digital Signal Processing and includes:- Understand structures of digital signal processing systems
- Design finite impulse response filters
- Design infinite impulse response filters
- Apply transforms to solve digital signal processing problems and spectral estimation - Apply digital signal processing to a range of real industrial problems.
Aims
This module is intended to provide students with a good appreciation of the mathematical concepts necessary to apply digital signal processing algorithms to a range of engineering problems.
Learning Outcomes
After completing the module the student should be able to:
1 Specify and design DSP Systems
2 Design FIR Filters
3 Design IIR Filters
4 Solve DSP problems and estimate spectra using appropriate transforms.
5 Apply DSP to a range of applications.
Outline Syllabus
Architecture requirements of DSPs,
Use of MATLAB & SIMULINK
FIR filter design: The choice of windows: fixed form v. adaptable form, Design of optimal filters
IIR filter design: analogue prototypes: Butterworth, Chebyshev, Elliptic. Bilinear transform.
Use of transforms in one of two dimensions: Fourier, Laplace, Z, Discrete Cosine Transform.
Non-parametric methods of spectral estimation, bias, resolution, Wavelets.
DSP systems applied to speech and image processing.
Indicative References
Ifeacher E.C., Jervis B.W. (2002) 'Digital Signal Processing: A practical Approach' 2nd Addison-Wesley 0 201 59619-9
Mitra, S.K (1998) 'Digital Signal Processing :A Computer-Based Approach' McGraw-Hill International Editions 0-07-115793-X
Oppenheim A.V., Oppenheim A.V., Buck J. R. (1999) 'Discrete-Time Signal Processing' Prentice Hall 0137549202
ENRTA3066 - Artificial Intelligence
Module Leader
Dr Karl Jones
Principal Lecturer
Engineering
James Parsons Buiding
Email: k.o.jones@ljmu.ac.uk
Dr. Karl Jones is a Principal Lecturer in at Liverpool John Moores University (LJMU). He received his BEng (Hons) in Electrical and Electronic Engineering in 1988. He was awarded his PhD in Fermentation Control Systems in 1995. His current research interests include the application of Artificial Intelligence and Evolutionary Computing to biotechnological processes and Control Systems. He is also investigating approaches to Education in Engineering. He has published over 80 papers in books, journals, and conferences; he also organises a number of international conferences.
During this module a range of artificial intelligence (AI) techniques will be studied. Case studies will illustrate the application of AI to engineering problems. Students will gain hands on use of implementing AI methods using computer software packages. It covers:
- Understanding of artificial intelligence and knowledge based systems
- Neural networks, multi-layer perceptron and back propagation
- Genetic algorithms, optimisation, mutation and evolution techniques
- Case study illustrations
Aims
To provide an introduction to a range of artificial intelligence (AI) techniques and how they can be applied to engineering and technological problems.
Learning Outcomes
After completing the module the student should be able to:
1 Design and apply AI and knowledge based systems to engineering applications.
2 Develop and evaluate a neural network application.
3 Appreciate optimization problems and their solution with genetic algorithms.
Outline Syllabus
Introduction to AI including definitions.
Neural nets: overview of network architectures and learning schemes, perceptron learning, multi-layer perceptron and backpropagation, implementation.
Genetic algorithms: optimisation and conventional techniques, data coding, reproduction, cross-over, mutation and evolution techniques.
Case studies will illustrate the application and performance of AI methods in engineering, e.g. modelling of systems and signals; pattern recognition; image processing.
Indicative References
Negnevitsky, M (2004) 'Artificial Intelligence: A Guide to Intelligent Systems' 3rd Edition Addison Wesley 0321204662
P Picton (2000) 'Neural networks' 2nd edition Palgrave 0-333-80287
R Beale & T Jackson (1990) 'Neural computing: an introduction' IOP Publishing Ltd. 0852742622
MATLAB and Neural Networks Toolbox
ENRTA3020 - Control Systems Design
Module Leader
Dr Barry Gomm
Reader in Intelligent Systems
Engineering
James Parsons Building
Email: j.b.gomm@ljmu.ac.uk
Dr Barry Gomm received the BEng first class degree in electrical and electronic engineering in 1987 and the PhD degree in process fault detection in 1991 from Liverpool John Moores University (JMU), UK. He joined the academic staff at JMU in 1991 and is a Reader in Intelligent Systems. He was coeditor of the book Application of Neural Networks to Modelling and Control (1993) and Guest Editor for special issues of the journals Fuzzy Sets and Systems and Trans Inst. MC. He has served on the organising committees of several national and international conferences. He has published more than 100 papers in international journals and conference proceedings. Dr Gomm is a member of the IEE, IEEE and the IEE Technical Advisory Panel for the Concepts for Automation and Control Professional Network. His current research interests include neural networks for modelling, control and fault diagnosis of non-linear processes; intelligent methods for control; system identification; adaptive systems; chemical process applications.
Introduction
This level 3 module extends level 2 concepts into continuous control design using frequency response, root locus and state variable methods. It extends level 2 concepts into discrete control system design by mathematical analysis and synthesis. It also introduces discrete time modeling and the concepts of self-tuning control. It includes:
- Single variable and multi variable control system design
- Application of state variables in multi variable system analysis
- Use of discrete transfer functions and discrete controller design
- Concepts of adaptive and self tuning control
- Fuzzy logic systems.
To extend the basic concepts of control into continuous design methodology, digital control systems and multi-variable systems.
To equip students with a comprehensive knowledge of the synthesis, analysis and design of continuous and digital control systems and multi-variable systems.
1 Utilise frequency response and root-locus techniques for single-variable control system design.
2 Understand how pulse transfer functions can be obtained from plant input/output data; and to use z-domain analysis and synthesis in case studies.
3 Understand the problems of multi-variable control system design
4 Understand the application of state variables in multi-variable system analysis
5 Analyse and synthesise case studies of real processes in the z-domain.
6 Obtain a discrete transfer function from process time input/output data
7 Understand the concepts of adaptive and self-tuning control
8 Demonstrate familiarity with CACSD packages.
9 Design a Discrete Controller to specification
Outline Syllabus
Frequency response design: The Nyquist criterion, analysis and design. Relative stability and theBode diagram. Closed-loop response. Sensitivity. Time delays. Design using lag, lead, lag-lead implementation. PID controller design. Nichols chart analysis and design.
Root locus design: The root locus concepts. Construction of root loci. Analysis and design techniques. Phase-lead and phase-lag design. PID controller design. Complementary root locus. State variable analysis and design. State models. Diagonalisation. Controllability and observability. Pole placement by state feedback. State estimation observers.
Optimal control design: Solution-time criterion. Control-area criterion. Performance indices. Zero steady state step error systems. Modern control performance index. Quadratic performance index. Ricatti equation. Analysis of systems using z-transfer functions.
Sampled data control: Use of z-transforms for closed-loop transient response. Stability analysis using bilinear transform and Jury method.
Design of digital controllers: Design to specification - deadbeat, pole assignment, first and second order normalised transient responses. Introduction to self-tuning controllers - pole assignment, minimum variance, etc.
On-line identification: On-line techniques for parameter estimation using least squares techniques - generalized, recursive, maximum likelihood, etc. Simulation of process plant and plant control systems.
Indicative References
Phillips and Harbor (1999) 'Feedback Control Systems' 4/e Prentice Hall 0139490906
Franklin, G.F (1979) 'Digital Control of Dynamic Systems' 3/e Addison-Wesley Pub Co 0201820544
ENRTA3026 - Process Control
Module Leader:
Professor Dingli Yu
Professor of Controls Systems
Engineering
James Parsons Buildings
Email: d.yu@livjm.ac.uk
Dr. Dingli Yu received B.Eng from Harbin Civil Engineering College, China in 1982, M.Sc from Jilin University of Technology (JUT), China in 1986, and the PhD from Coventry University, U.K. in 1995, all in Control Engineering. Dr. Yu was a lecturer at JUT from 1986 to 1990, a visiting researcher at University of Salford in 1991, a post-doctoral research fellow at Liverpool John Moores University from 1995 to 1998. He joined LJMU in 1998 as a senior lecturer and was promoted to a reader in 2003, and to the professor of Control Systems in 2006. He is the associate editor for two journals, IJISS and IJMIC and, the IFAC SAVEPROCESS Committee member, and IPC member for many international conferences. His current research interests include fault detection and fault tolerant control of bilinear and nonlinear systems, adaptive neural networks and their control applications, model predictive control for chemical processes and engine systems.
Introduction
This Level 3 module describes the analysis and design principles of closed-loop control of process systems.
It includes:
- Analysis of multiple feedback loops
- Feedback control of systems with large dead-time
- PID control systems
- Analysis of non-linear process systems
- Process control case studies, distillation, fermentation, pH systems.
To
appreciate the problems associated with the design of closed-loop control of process systems. To understand the principles of cascade, feed forward and ratio control. To analyse non-linear process systems, systems containing large dead-time and coupled multi-loop systems.Learning Outcomes
After completing the module the student should be able to:1 Demonstrate an understanding of the principles of cascade, feed forward and ratio control of process plants, with typical applications.
2 Formulate the system equations of certain processes using large scale and linearised, deviation variable analysis.
3 Demonstrate an awareness of strategies for controlling systems possessing dead-time, inverse response and interaction properties.
Outline Syllabus
System equations for a number of unit processes, e.g. temperature, level, flow, chemical reaction.
Analysis of multiple feedback loops. Disturbance transfer function. Signal flow, Mason's Rule. State variable formulation. Types of control strategy and their relative merits: cascade, split-range, feedforward and ratio control.
Feedback control of systems with large dead-time and inverse response; Smith prediction. PID control, frequency response stability criteria; Nichol's charts. Interaction and decoupling of control loops.
The analysis of non-linear process systems e.g. liquid-level. Use of linearisation and deviation variables in deriving transfer functions. Effect of choice of system elements (e.g. control valve characteristics) on system performance.
Case studies such as distillation, fermentation, pH systems.
Indicative References
Stephanopoulos, G. (1985) 'Chemical Process Control-An Introduction to Theory and Practice' Prentice Hall
Luyben, W. (1990) 'Process Modelling, Simulation and Control for Chemical Engineers' McGraw-Hill
Shinskey, F.G. (1990) 'Process Control Systems' 2nd McGraw-Hill
