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MSc Advanced Control and Systems Engineering / Course details
Year of entry: 2024
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Course unit details:
Applied Control and Autonomous Systems
Unit code | EEEN60122 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | No |
Overview
1) Applied Control
1.1 Introduction
1.2 Actuators and sensors
1.3 Kinematics, path planning, and trajectory tracking
1.4 Dynamics
1.5 Advanced control for path planning and trajectory tracking
1.6 Applications using digital twin and physical twin.
2) Autonomous Systems
2.1 Introduction to probabilities
2.2 Introduction to autonomous systems
2.3 Uncertainty propagation in autonomous systems
2.4 Map-based localisation
2.5 Mapping
2.6 Introduction to SLAM (Simultaneous Localisation and Mapping)
2.7 Reactive navigation
2.8 Path-planning
2.9 Applications using digital twin and physical twin.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Nonlinear and Adaptive Control Systems | EEEN60252 | Pre-Requisite | Compulsory |
Robotic Manipulators | EEEN62012 | Pre-Requisite | Compulsory |
Aims
The aim of this course if to enable the studnets to solve real life problems and be prepared for the multi-trillion job market in Control Systems and Autonomous Systems. To make this a reality, this course is addressed in a different manner. The students will have access to a mobile robot for the whole duration of the course, i.e., one robot-one student. The idea of "the lab in a robot" is to stimulate the students to use their imagination and creativity anytime and anywhere, in their own time and not to be restriced by the access to the lab equipment.
Learning outcomes
ILO1 - Summarise the main levels of autonomy in mobile robotics, evaluate the uncertainties and how they propagate between the levels of sutonomy and illustrate them with examples. (Developed and assessed).
ILO2 - Describe different strategies for localisation, mapping, navigation, and path-planning and assess the benefits and limitations of different proprioceptive and exteroceptive sensors. (Developed and assessed).
ILO3 - Design and implement nonlinear control algorithms for mobile robot navigation. (Developed and assessed).
ILO4 - Analyse, evaluate and implement in Python the algorithms for complex robotic applications, simulated and real mobile robots i.e., for digital twin and for physical twin. (Developed and assessed).
ILO5 - Evaluate the environmental and societal impact of complex control systems. (Developed and assessed).
ILO6 - Reflect the effectiveness of individual and teamwork. (Developed and assessed).
Assessment methods
Method | Weight |
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Other | 50% |
Written exam | 50% |
Written Examination (50%)
2 hours, four questions, answer all questions.
Lab and Coursework Assignments (50%)
Report and movie presentation. CW Report 1 15%, CW Report 2 15%, movie submision for team assessments 20%.
Feedback methods
Written Exam
Feedback is provided after exam board.
Lab and Coursework Assignments
Simulation based using the digital twin. Hardware based using the physical twin. .
Recommended reading
For Applied Control
1. K. Astrom and T. Hagglund, "PID Controllers: Theory, Design and Tuning", 2nd edition.
2. V.I. Utkin, "Sliding modes and their application in variable structure systems", 1978.
3. E. Slotine and W. Li, "Applied Nonlinear Control", Prentice-Hall, 1991.
4. L.Ljung, "System identification. Theory for the user", 1987.
For Autonomous Systems:
1. R. Siegwart and I. R. Nourbakhsh, Introduction to Autonomous Mobile Robots, 1st ed. Cambridge, Massachusetts: The MIT Press, 2004.
2. Mustafa Abdalla Mustafa, M, 2017. Guaranteed slam an interval approach. [PhD thesis] Manchester: University of Manchester.
3. Bruno Siciliano and Oussama Khatib. 2007. Springer Handbook of Robotics. Springer-Verlag New York.
4. S Thrun, W Burgard and D Fox, Probabilisitc robots, MIT press, 2005. 93
5. IM Rekleitis, A particle filter tutorial for mobile robot localization trcim-04-02, Centre for Intelligent Machines (2003)
Study hours
Scheduled activity hours | |
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Lectures | 30 |
Practical classes & workshops | 12 |
Tutorials | 6 |
Independent study hours | |
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Independent study | 102 |
Teaching staff
Staff member | Role |
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Alexandru Stancu | Unit coordinator |