MSc Advanced Control and Systems Engineering

Year of entry: 2024

Course unit details:
Applied Control and Autonomous Systems

Course unit fact file
Unit code EEEN60122
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
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
Lectures 30
Practical classes & workshops 12
Tutorials 6
Independent study hours
Independent study 102

Teaching staff

Staff member Role
Alexandru Stancu Unit coordinator

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