The module is to introduce advanced system analysis and design techniques to the students and to develop the skills of considering engineering problems from system point of view.

The aims of the module are:
To learn the skills required for system modelling and simulation.
To extend the students knowledge from time-driven system to even-driven system modelling and simulation, which covers modelling and simulation of stochastic processes.
To understand the principle of advanced control systems. Understand principles of basic adaptive and learning systems and their applications.
Select appropriate adaptive systems and/or learning algorithms to deal with a specific engineering problem.
Develop software packages using MATLAB to resolve an adaptive and/or learning problem.
Gain their own knowledge of the subjects of adaptive and learning systems for further development.

(LO1) After successful completion of the module, the student should have: An understanding of how time and event driven systems can be represented by mathematical modules.
An understanding of how computer simulation can be implemented to help system analysis and design.
An appreciation of how computer-aided design and simulation tools operate.
An understanding of how random number and random process can be simulated.
An understanding of discrete time Markov process modelling and simulation.
An appreciation of the system optimisation.
The principle of advanced control system design.
An appreciation of the advantages of system identification approached to problems of industrial modelling and control and adaptive controller design by contrast to the traditional methodologies.
A familiarity with system identification and parameter estimation of dynamic systems.
An understanding of the system identification and adaptive control techniques.
An ability to use the MATLAB software to model a linear dynamic system and design an adaptive controller.
An appreciation of how adaptive control theory can be applied to various industrial systems.
A basic understanding of stochastic automata and their applications.

(S1) on successful completion of the module, students should be able to show experience and enhancement of the following key skills: Independent learning
Problem solving and design skills

(S2) After successful completion of the module, students will have skills to develop software programs for complicated mixed time-and-event-driven systems. on successful completion of this module the student should have practical skills of using MATLAB System Identification Toolbox to achieve the system modelling of basic engineering systems and to design a basic adamptive learning system for engineering problems.

(S3) After successful completion of the module, the students should be able to demonstrate ability in applying knowledge of the module topics to: Develop mathematical models for both time-driven and event-driven systems. Analyse the systems described by Markov process. Model, simulate, and validate random processes. Design simulation programs for particularly specified systems. Understand the methods of system optimisation and adaptive control design. On successful completion of this module the student should be able to pursue the further study by themselves in this subject and relevant areas.

(S4) After successful completion of the module, the student should have: An understanding of how time and event driven systems can be represented by mathematical modules. An understanding of how computer simulation can be implemented to help system analysis and design. An appreciation of how computer-aided design and simulation tools operate. An understanding of how random number and random process can be simulated. An understanding of discrete time Markov process modelling and simulation. An appreciation of the system optimisation. The principle of advanced control system design. An appreciation of the advantages of system identification approached to problems of industrial modelling and control and adaptive controller design by contrast to the traditional methodologies. A familiarity with system identification and parameter estimation of dynamic systems. An understanding of the system identification and adaptive control techniques. An ability to use the MATLAB software to model a linear dynamic system and design an adaptive controller. An appreciation of how adaptive control theory can be applied to various industrial systems. A basic understanding of stochastic automata and their applications.

Concepts of systems
Concepts of systems (linear and nonlinear)
Mathematical descriptions of systems, System approach.

Modelling and simulation of time driven systems
Time driven system modelling, simulation model diagram.
Numerical solutions of initial value problems
Software implementation of simulation models and special input signals
Introduction to complex system simulation.

Stochastic generator and data representation
Stochastic process; time series
Modelling and simulation of random numbers
Modelling and simulation of random processes
Stochastic data representation, simulation of disturbance signals

Markov process simulation
Discrete time Markov processes, modelling discrete time Markov processes.
Simulation of descrete time Markov processes, continuous time Markov processes.

Modelling and simulation of event driven systems
Modelling event driven systems
Queuing theory

Neural Net work based model identification
An introduction to the concepts of NN based model
Training of NN and applications in adaptive control

System indentification
Black box;
Probing signals;
Parametric estimation;
Recursive least squares;
Kalman filter;
Prediction.

Self-tuning control
Self-tuning regulators;
Generalised minimum variance control;
Smith predictor;
Pole placement.

Model-reference adaptive control
Model reference adaptive systems;
MIT Rules;
Lyapunov function design;
Variable structure systems;
Design of industrial adaptive control systems.

Due to Covid-19, one or more of the following delivery methods will be implemented based on the current local conditions and the situation of registered students.
(a) Hybrid delivery, with social distancing on Campus
Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average two per week

Teaching Method 2 - Synchronous face to face tutorials
Description: Tutorials on the Problem Sheets and Assignment
Attendance Recorded: Yes
Notes: On average one per week

(b) Fully online delivery and assessment
Teaching Method 1 - On-line asynchronous lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On average two per week

Teaching Method 2 - On-line synchronous tutorials
Description: Tutorials on the Problem Sheets and Assignment
Attenda nce Recorded: Yes
Notes: On average one per week

(c) Standard on-campus delivery with minimal social distancing
Teaching Method 1 - Lecture
Description: Lectures to explain the material
Attendance Recorded: Yes
Notes: On average two per week

Teaching Method 2 - Tutorial
Description: Tutorials on the Problem Sheets and Assignment
Attendance Recorded: Yes
Notes: On average one per week