Why you should study this course
This professionally accredited course covers areas ranging from classical control system design to optimal, adaptive and intelligent control systems, such as proportional + Integral + Derivative (PID) control, Smith Predictor, Model Predictive Control (MPC), state variable feedback pole placement.
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We aim to develop your skills required to design, analyse and simulate automatic control systems. Teaching is practical in nature with laboratory sessions in specialist software, including MATLAB/Simulink and LabVIEW. Following these simulation studies, you should be able to deploy control systems on specialist hardware.
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We aim for dissertation project work across this course to be based on real-life situations to gain familiarity of methods and techniques from our industrial partners, or to support on-going research projects in automotive control systems, cooperative and autonomous vehicles, and preventive maintenance applied to medical devices.
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The course aims to draw on the industrial experience of the teaching team who have rich experience and collaborations with industry in the UK and abroad, in practical control engineering research activities covering various applications for automotive, health rehabilitation, robotics, and power systems applications, to mention a few (please note staff may be subject to change). This opens wide opportunities for students in this course to get involved in industrial projects2 and/or do a placement in industry2 for their dissertation project, which also gives opportunities for students to network and work with future employers.
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Where appropriate, research seminars, including staff, research students, and external guest speakers from industry are invited to provide a lecture/presentation to supplement and reinforce the practical relevance of the material. In addition, students are also encouraged to attend externally organised seminars, workshops, training, or internships to gain wider knowledge and experience, as well as build their professional network. Please note that these opportunities2 are subject to availability, competitive application, visa considerations and additional fees may apply.
What you'll study
The MSc Control, Automation and Artificial Intelligence course is designed to provide a unique route for postgraduates to top up their background with more specialised knowledge and skills in the rich field of control systems and engineering relevant to various practical industries as well as research in academia.
The course covers the three closely relevant fields via option pathways. Those pathways are Control Engineering Pathway, Automation Pathway and Intelligent Systems Pathway. All three pathways commence with common modules in Semester 1, to offer a greater degree of flexibility for students to explore their main interest as they progress within the course. Then each pathway will deliver specialised modules in Semester 2, allowing students to focus on specialised subjects based on their interest. This specialisation then continues to be developed within the students’ dissertation project, which is carried out in Semester 3.
Pathway 1: Control Engineering
This pathway focuses on analytical aspects of control engineering. It is designed for students interested in developing their careers or upgrading their knowledge in areas that require accurate and robust control design for possibly unstable systems, with emphasis on implementation. This course aims to provide excellent preparation for those wishing to pursue their careers as a control engineer or technical specialist in industrial fields such as automotive, aerospace, energy, and other electrical and mechanical engineering based industries, as well as those who are interested in pursuing postgraduate research.
Pathway 2: Automation
This pathway focuses on automation technology and practice in industry. It is designed for students interested in developing their careers or upgrading their knowledge in areas including control in manufacturing process, systems design, systems integration, system operation, with emphasis on implementation. This course aims to provide excellent preparation for those wishing to pursue their careers as a control engineer in industrial automation fields such as in oil and gas, power distribution, chemical and food industry and other process control based industries, as well as those who are interested in pursuing postgraduate research.
Pathway 3: Intelligent Systems
This pathway focuses on decision making based control approaches. It is designed for students interested in developing their careers or upgrading their knowledge in areas such as system designer or system developer with emphasise on implementation of control algorithms that involves optimisation and learning. This course aims to provide excellent preparation for those wishing to pursue their careers as a control engineer in various autonomous systems applications, such as autonomous vehicle or driverless cars, intelligent robotics, as well as those who are interested in pursuing postgraduate research. This pathway is suitable for students with software engineering or computing background, or other engineering background with particular interest in coding or programming.
Modules
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Linear Control Systems Analysis and Design - 15 credits
This module aims to equip you with fundamental knowledge and skills in control system analysis and design, providing the background to study other related or more advanced topics in Control Engineering.
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Programming and Simulation for Control - 15 credits
This module prepares you to solve real-world engineering and problems using MATLAB, Simulink, Stateflow and LabVIEW. The module will be delivered using activities and problems inspired by research projects that have been and are currently being carried out at Coventry University.
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System Modelling and Identification - 15 credits
The purpose of this module is to introduce you to the techniques and approaches required to construct mathematical models of dynamical systems, particularly data-based modelling. Modelling of a system takes about 70% of the effort in the whole process of control system design. The quality of a model significantly determines the quality of the controller designed based on the model.
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Automation and Robotics - 15 credits
The aim of this module is to provide an understanding of the application of automation techniques and mechatronics in manufacturing. Topics covered in the lectures include the types of robots, robot programming, programmable logic controllers, sensors, vision systems, motion control, safety systems, and design for manufacture.
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Optimisation and Adaptive Control - 15 credits
This module focuses on how applied optimisation techniques are used in control engineering and other engineering problems in general.
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Data Acquisition and Embedded Control - 15 credits
The module will concentrate on providing you with experience of the industry standard software and hardware tools used in real time control, namely dSPACE together with Mathworks products, LabVIEW from National Instruments and DSP type micro-controllers together with Mathworks products.
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Postgraduate Control Dissertation Project - 50 credits
This module aims to provide you with an opportunity to develop your ability to plan and control your own work at master’s degree level. The activities appropriate for the development of these skills may be described in broad terms, encompassing investigation, synthesis, analysis, communication, project planning and management. You will exploit the theoretical, computing, technical and management skills learned during the course to design, develop, implement and document a control solution to a research or industrial problem.
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Leading Strategic Change through Creativity and Innovation - 10 credits
This module aims to provide you with a framework of knowledge and understanding of how to manage change using creativity and innovation in different types of organisational scenarios. You will be given the opportunity to develop an innovative framework to deliver a change management strategy in a changing organisational context.
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Control Engineering Pathway
Non-Linear Control Engineering - 15 credits
The module Nonlinear Control Engineering aims to introduce the concepts of nonlinear control systems, with attention being focused on typical nonlinearities found in various applications in practice. In particular, desirable and undesirable properties of nonlinear elements in a control system will be investigated.
Advanced Control Systems Analysis and Design - 15 credits
The purpose of this module is to introduce you to advanced control principles and application specific to the automation and control industry with emphasis being placed on the use of microcomputer/microcontroller to implement the control techniques. It is designed to both build on and consolidate your knowledge and skills on the linear control systems analysis and design and equip you with the skills to perform the controller design using software such as MATLAB/ Simulink.
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Automation Pathway
Advanced Control Systems Analysis and Design - 15 credits
The purpose of this module is to introduce you to advanced control principles and application specific to the automation and control industry with emphasis being placed on the use of microcomputer/microcontroller to implement the control techniques. It is designed to both build on and consolidate your knowledge and skills on the linear control systems analysis and design and equip you with the skills to perform the controller design using software such as MATLAB/ Simulink.
Digital Signal and Image Processing - 15 credits
This module will first revise/introduce the fundamentals of the analysis of digital signals and systems. This will then lead to the development of higher-level signal processing techniques and filters design before applying them to some problems to demonstrate their applications. Additionally, the concepts of digital image processing and image enhancement techniques will be introduced.
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Intelligent Systems Pathway
Digital Signal and Image Processing - 15 credits
This module will first revise/introduce the fundamentals of the analysis of digital signals and systems. This will then lead to the development of higher-level signal processing techniques and filters design before applying them to some problems to demonstrate their applications. Additionally, the concepts of digital image processing and image enhancement techniques will be introduced.
Artificial Neural Network - 15 credits
This module aims to introduce you to the concepts used in neural networks and their application in solving real-world problems. The main topics covered include biological motivations of neural networks, different approaches including the main supervised and unsupervised neural network architectures, static and temporal learning approaches, data collection and preparation methods for neural network learning, applications of neural networks, current trends and future developments.
