Short Courses in Control and Systems Engineering
"This activity is appropriate for the maintenance or enhancement of a competence relevant to an individual's professional development."
The Short Courses Programme is intended as continuing professional development for engineers, and results from a comprehensive consultation and liaison with industry and individual engineers.
Individuals can choose to attend any number of the range of the Masters-level modules as a series of short courses. These can also be taken as Continuing Professional Development - with no assessment undertaken.
Short Course modules and module summaries
| Module code, title and summary | Number of weeks |
|---|---|
| ACS6101: Foundations of Control Systems The module ACS6101 provides all students with appropriate pre-requisite knowledge for future modules. It also gives students a broad exposure to the fundamentals of systems modelling, classical control systems analysis and design and simulation. |
6 |
| ACS6102: State-space, Optimal Control and Nonlinear Systems The module ACS6102 introduces basic and advanced concepts of state space analysis and design together with topics in optimal control systems theory. Provides tools and methods for analysis of non-linear systems and introduces some recent global method in non-linear systems theory. Rigorous mathematical exposure to topics such as calculus of variations and Pontryagin's Maximum Principle is dealt with in parallel to the use of computer-based MATLAB toolboxes for state-space design. |
3 |
| ACS6103: Signal Processing and Estimation The module ACS6103 introduces students to the concepts and methods of systems identification, signal processing and estimation. It shows the practical application of the methods to several industrial case studies, provides background theory and practical methods for the design of discrete filters, and demonstrates the main ideas of low-level image processing. |
3 |
| ACS6115: Embedded Systems, Reliability and Fault Diagnosis This unit covers topics in embedded control and in systems reliability and fault diagnosis. Embedded control systems, which are electronic systems that include a microcontroller to perform specific dedicated applications, are nowadays in widespread use. This module places recent technological advances within a historical context and introduces modern issues of design, implementation, and validation of hard and soft real-time embedded control systems. It includes the basic building blocks of real-time embedded control, performance measures, task scheduling and advanced software topics like interrupts. There is also a brief introduction to real-time operating systems. Laboratory projects allow students to implement and evaluate aspects of embedded systems design using an ARM microcontroller. In systems reliability and fault diagnosis, basic and advanced concepts of systems reliability and health monitoring are introduced. Topics covering both qualitative and quantitative aspects of failure modes and their reliability will be discussed. Fundamental theory of change detection is introduced as the basis on which further advanced methods are analysed. Model based and data driven diagnosis methods are introduced in parallel to the use of computer-based MATLAB routines for specific detection tasks on simulated and real laboratory systems. |
3 |
| ACS6116: Advanced Industrial Control This unit comprises concepts of active noise and vibration control (ANVC) and predictive control. The unit on ANVC introduces the basic concepts of analysis and design of active control systems using both mechanical vibration and acoustic signals as real-world examples. This unit on predictive control addresses control design techniques based on prediction, usually denoted model based predictive control (MPC). |
3 |
| ACS6117: Intelligent Systems This module provides an introduction to the theory and practice of machine learning and data modelling, and to fuzzy logic within a control and systems engineering context. We will look at the underlying principles of machine learning, data modelling and fuzzy logic, the advantages and limitations of the various approaches and effective ways of applying them in systems and control engineering, with the aim of making students appreciate the merits of the various technologies hence introduced. |
3 |
| ACS6118: Robotics and Multi Sensor Systems This module covers topics in the closely related disciplines of robotics and multi-sensor systems. The module initially covers robotic systems with an emphasis on case study driven material on the technical and theoretical aspects of robotics. Multi-sensor systems are now a common feature of many engineering applications including robotics. The second part of the module will introduce multi-sensor systems and approaches to data fusion. |
3 |