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 applicaitons.
• 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.

After successful completion of the module, the student should have:

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 Network 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.

Lecture -

Tutorial -

Assessment -

Formal Examination

Other -

Coursework, case study