UNIT TITLE: Control Engineering
CREDIT POINTS: 20
Parent Course: BEng (Hons) Electronic Engineering
Delivering Faculty: FOT
Level (on parent course): 3
Date validated: March 2003
TOTAL STUDENT WORKLOAD
Students are required to attend and participate in all the formal timetabled sessions for the
unit. Students are also expected to manage their directed learning and independent study in
support of the unit.
Where normal timetabled sessions do not take
place, additional directed learning may be
provided, and/or students are expected to undertake additional independent learning
Signals & Circuits I
Signals & Circuits II
Instrumentation and Control systems
This unit is designed to develop a sound understanding of the operation of modern control
systems. The study of continuous control systems in Instrumentation and Control (Level 2) is
extended to consid
er the design of analogue controllers and discrete
time systems. It also
considers in detail the major areas of intelligent control. Delivery will place equal emphasis
on traditional approach to system modelling and control. The traditional techniques are
extended to cover modern techniques required for the analysis/design of sampled
systems. Case studies drawn from engineering journals will be used to illustrate the operating
characteristics and relative merits of various types of intelli
gent control applicable to real
world industrial control problems.
Understanding and knowledge
Relate the operation of the modern control system and the AI
methodologies applicable to industrial control.
Obtain a mathematical model of a physical system and investigate its behaviour.
Analyse various techniques and methods employed for determining stability of
Discuss and critically appraise the use of AI techniques for industri
Synthesise an AI
based solution to an industrial control problem.
Implement and evaluate the performance of a continuous/sampled
systems and AI
based control system using MATLAB.
AREAS OF STUDY
Mathematical modelling of physical systems. Transfer functions, signal flow graphs and their
applications. Time and frequency domain analysis of continuous control systems. Root
techniques and compensator design. Three term controllers; ana
lysis and design.
The sampling process. Representation of sampled
data control systems. The z
transform. Time and frequency domain response of sampled
data systems. Stability
consideration of a sampled
Overview of artificial intelligence in engineering.
Tree search. Calculus
based search techniques: Newton
Raphson and gradient descent.
Stochastic search techniques: simulated annealing and genetic algorithms.
The Artificial Neural Networ
k. Feedforward networks, learning using the delta rule and
propagation. Feedback networks. Industrial applications of neural networks.
Scheduling by critical path analysis. Use of simulated annealing and genetic algorithms.
Deterministic reasoning, propositional and predicate logic. Reasoning with uncertainty.
Possibilistic and fuzzy reasoning.
based (expert) systems. Industrial applications of fuzzy systems.
TEACHING AND LEARNING STRATEGY
The core theoretical
concepts of the unit are introduced through a series of lectures. These
concepts are then further developed and applied through small group teaching sessions where
a series of case studies will be presented, drawn from current engineering practice.
shops will provide practical experience in the design and simulation of control systems
to satisfy an engineering specification. MATLAB software will be employed to illustrate
application of the methodologies considered in lectures and workshops. Investiga
in the workshop will require the student to utilise an appropriate computer package (ie
MATLAB) to analyse the behaviour of continuous/sampled
data control systems appropriate
world applications. Computer simulation will be used to in
vestigate important AI
techniques; for example stochastic search using simulated annealing or genetic algorithms
and training multi
layer feedforward neural networks. An investigative assignment will
require the student to propose and critically appraise
based solution to an industrial
Workshops and Logbook
e assessment instruments for this unit are investigative assignment and workshops and a
two hour examination. The assignment and the workshops will provide the assessment of
Learning Outcome 5, while Learning Outcomes 1, 2, 3 and 4 are assessed by a formal
Control Systems Engineering
Norman S Nise
ing Company, Inc. 1995.
Mechatronics: The Design of Intelligent Machines
Concepts in Artificial Intelligence
Jeffrey Johnson and Philip Picton
Modern Control Systems
Richard C Dorf
and Robert H Bishop
Applied Digital Control: Theory, Design and Implementation
J R Leigh
C C Bissell
Chapman & Hall, 1994.
A Course in Fuzzy Systems and Control
Prentice Hall 199
Intelligent Control Systems
N K Sinha and M M Gupta (Eds)
IEEE Press 1996
An Introduction to Artificial Intelligence
Janet Finlay and Alan Dix
UCL Press 1996
Artificial Neural Networks: Concepts and Control Applications
V Rao Vemuri (ed)
Society Press 1992
Neural Network Applications in Control
G W Irwin, K Warwick and K J Hunt (Eds)
Institution of Electrical Engineers 1995
Industrial Applications of Fuzzy Logic and Intelligent Systems
John Yen, Reza Langari and Lotfi A Zadeh (Eds)
IEEE Control Systems Journal.
IEEE Tranasactions on Automatic Control.
IEE Computing and Control Engineering Journal
Control Engineering Journal
Computers in Industry
Expert Systems Journal
IEEE Transactions on Neural Networks
ial Intelligence Journal
Unit Author: A A Wardak
Date Prepared: February 2003