The courses all provide you with the tools and knowledge necessary to create autonomous systems which are smart enough to control and regulate themselves.
This means that you will learn how a machine can use its sensors to properly sense the environment around it, how to choose and place these sensors for the best results, and how your creation will use this data to do whatever it is supposed to do.
Companies working with complex electronic and electromechanical systems will value your skills in handling complex design and development of autonomated systems. Thus, many professional fields are open to you. You may obtain employment in design, development, construction and/or test of control systems and autonomous systems.The master's degree in Control and Automation is offered by Aalborg University
Control and Automation is a two-year master's programme (120 ECTS credits). Below are descriptions of the courses on the various semesters. For further information, please see the curriculum for Control and Automation.
The projects and courses on each semester are centred on a general theme and are aimed at giving you a comprehensive and thorough insight into that particular area. Thus, the semester projects are named after the semester theme.
A stochastic process is a mathematical model that can be used to describe random (or unpredictable) mechanisms evolving in time. Many real-world examples of random phenomena can be given: weather phenomena (lightning discharges, rainfall, temperature), stock markets, internet traffic, noise processes, radioactive emission and soforth. Good models of stochastic processes allow engineers to design and optimise our systems – in particular making them able to operate in unpredictable and noisy conditions. On this course, we discuss different random processes and their properties. We also consider methods to estimate parameters of these – and even methods to predict what is going to happen in the future.
Distributed Real Time Systems
This course is aimed at explaining how and why distributed systems work. You can find such systems for example in cars, production lines or even whole factories where different sensors and controllers need to exchange information in real time. In order to do this, there are several specialized types of networks or buses as they are called. These will be explained in detail during the course, you will be provided with the theoretical tools needed to analyse the system off-line. In addition, you will be presented with a set of computer simulators which will help you design and manage a distributed real time network. You will also learn about reliability concepts.
This course consists of several parts. In the first part, the so-called state space approach to feedback design is presented. This approach embarks from physics-based models of the system to be controlled. One of the underlying principles is to use the model to infer from behaviour of measured variables to behaviour of hidden variables. Similarly, by applying the model, hidden variables can be manipulated indirectly by using causality and manipulating variables that can be directly accessed.
Another part of the course focuses on control methods implemented in very large multivariable systems, where you can have multiple feedback loops, feed-forward loops, set point controllers, etc. There will also be discussions about system stability, transfer function poles, controller gain design with the use of the root locus technique as well as lead and lag compensation by placing additional poles and zeros.
Network Controlled Systems (project theme)
Each semester, you will get to form groups of 4-6 students. Then, you will get to choose a project from a list of proposed themes. These will be known about a month in advance.
*For students who are here for the first time, the project will only be allocated 14 ECTS, and the remaining one point will be awarded for a course called “Project Oriented Problem Based Learning” (PBL) which will give you more insight into the PBL model implemented here at AAU, how groups work and why it is so important to us.
This course comprises an introduction to optimal and robust control. In the optimal control part, emphasis is on practical application. This subject is presented through discrete time linear quadratic regulator (LQR) and covers the following subjects: Solving discrete time LQR optimal control problems using dynamic programming; extension of the standard LQR problem with reference following; introducing disturbance rejection and integral control in the LQR problem by extension of the state space; and solving LQR problems with constraints using model predictive control (MPC). On the more theoretical side, the course contains a short introduction to nonlinear optimal control problems via Pontryagin’s maximum principle and the Hamilton- Jacobi-Bellman equation. The robust control part covers stability and performance conditions for both SISO and MIMO systems with norm bounded multiplicative and additive perturbations and H-infinity performance. Controller synthesis is touched via H-infinity optimisation and mu-synthesis. Linear matrix inequalities (LMI's) and the bounded real lemma are introduced in connection with analysis and synthesis of a range of optimal and robust control problems.
Robot Vision (elective)
You will be presented with the basics of robotics: Danavit-Hartemberg coordinate transformations, forward and backward kinematics, etc. Also, there will be lectures about image processing such as colour detection, shape detection, orientation detection, filtering, blob analysis, etc. This course also presents several graph theory concepts as well as fuzzy logic programming. The best part is the project: you will design a system that detects Lego bricks, picks them up with an industrial robot and builds simple stacks of 3 blocks (2013 theme). You can see the results from one group here:
Fault Detection, Isolation and Modelling (elective)
All real-life systems will at some point be affected by one or more faults, ranging from external faults such as an object causing physical damage to internal faults e.g. a programming error in a robot control unit. This course enables you to analyse the effect of faults on dynamic systems and distributed network-coupled systems as well as introducing you to different methods for making such systems more robust towards faults.
Systems of systems/complex systems
This course introduces a discrete-time version of the Pontryagin maximum principle (PMP), which give necessary conditions for a controller to be optimal with respect to a general optimal control problem. Moreover, generalisations of the optimal control problem to which the standard PMP can be applied will be discussed under the headlines; Differential Games (Zero-sum and Nonzero-sum differential games) and Distributed Parameter Systems (Distributed parameter maximum principle).
Machine Learning (elective)
The course gives a comprehensive introduction to machine learning which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realised by presenting methods and tools proven valuable and by addressing specific application problems.
Projects within the course will enable you to apply the taught methods to solve concrete engineering problems and will give you competencies in analysing a given problem and identifying appropriate machine learning methods to the problem.
Non-linear Control Systems (elective)
The course comprises an introduction to nonlinear control systems. It discusses the notions of stability such as stability in Lyapunov sense, asymptotic and exponential stability. It puts forward tests for checking if a system is stable based on behaviour of a so-called Lyapunov function. Focus is on geometric methods: observability and controllability tests based on Lie algebras and feedback linearisation. Feedback linearisation is a pure geometrical method that helps to find a certain map which translates a nonlinear system into a linear one. The course introduces nonlinear techniques within observer design and sensor fusion as an extended Kalman filter, an unscented Kalman filter and particle filters. Last, but not least, the elements of hybrid control will be introduced; herein, the notion of a hybrid automaton, bisimulation, formal verification of control and hybrid systems, stability and control of switched systems.
Control of Complex Systems (project theme)
We are currently gathering new project ideas and examples to post here.
The fourth semester is fully dedicated to completing your master’s thesis. Thus, there are no courses on this semester.
Examples of master's theses
"Preview-based Asymmetric Load Reduction of Wind Turbines"
Fatigue loads on wind turbines caused by an asymmetric wind field become an increasing concern when the scale of wind turbines increases. This thesis presented a preview-based approach to reduce asymmetric loads by using Light Detection And Ranging (LIDAR) measurements. A Model Predictive Controller (MPC) that utilised a preview history of the wind obtained from the LIDAR was developed. The controller was based on a model with individual pitching of the blades, so that the asymmetric loads could be reduced by cyclic blade pitching. Using a transformation of moments acting on the blades, the controller is able to determine the pitching of the blades. This was done while still maintaining a given power reference and satisfying a set of actuator constraints. The designed controller was tested on a 5 MW wind turbine in the FAST simulation software and compared to the same controller without LIDAR data and a baseline controller for the turbine. The results showed that MPC without LIDAR performed similarly to the baseline controller and that MPC with LIDAR was able to reduce the asymmetric loads while still maintaining the power reference.
"Navigation Solution for Marine Applications using MEMS-based Sensors and GPS"
This thesis concerned the estimation of the position, velocity and attitude using inertial sensors, magnetometer and GPS. The purpose was to apply a model-based estimation filter to fuse these sensor inputs together to form estimations of the states of interest. This thesis was made in collaboration with the company: CDL - inertial engineering. The main hardware platform is a MiniSense2 (MS2), produced by CDL. The sensors on the MS2 platform consist of a 3-axis gyroscope, a 3-axis accelerometer and a 3-axis magnetometer. These are all low-price MEMS-based sensors. In this thesis, it was chosen to base the design of the navigation solution on a loose coupling between the sensors of the MiniSense2 and GPS. The estimation filter utilised in this was based on the structure of the extended Kalman filter. Differential equations regarding motion on the rotating earth were derived together with models of the sensors which included the assumption of additive biases of the MS2 sensors. The thesis also concerned models of the gravitational field and magnetic field of the earth.
"Safe Operation and Emergency Shutdown of Wind Turbines"
As the control systems and mechanical structures of wind turbines have become increasingly complex, it has simultaneously become more difficult to guarantee that a wind turbine structure is not damaged in any given situation. To avoid damage to the wind turbine, a safety supervisor system which can initialise an emergency shutdown should be implemented. The purpose of a safety supervisor is to keep the components of the wind turbine from being damaged. This thesis considered the design of a safety supervisor system able to guarantee the safety of complex wind turbine systems. In particular, multivariate safety supervisor systems were considered. This was done using the concept of safety envelopes in which the system can be shut down without structural damage.
"Supervisory Control of 4WD Vehicle"
In this thesis, a 4WD RC car driven by four individual electric motors was constructed. To compensate for missing differential(s) and other hardware common on vehicles with a thermal combustion engine, a supervisory control, controlling the torque and limiting the slip on each wheel individually was designed. To estimate the states of the RC car, an extended Kalman filter was developed in three parts. This was updated by eight accelerometers placed on the chassis and a tachometer in each electric motor. The torque and slip of each wheel were controlled using feedback linearisation combined with PID controllers. Data from the sensors on the car were collected, and the Kalman filter and supervisory control were evaluated in a simulated environment. The result showed that the controller worked, but the sensor noise on the car must be reduced.
You can apply until:
Always verify the dates on the programme website.
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The TOEFL – or Test Of English as a Foreign Language – offers an internet-based test (iBT). The final, overall iBT score ranges between 0 and 120, and includes a scaled average from the four components (reading, listening, speaking, and writing). Read more about TOEFL (iBT).
The following programmes directly qualify for the master's in Control and Automation:Full bachelors
Applicants with other backgrounds will receive individual assessment by the Study Board of Electronics and IT.
StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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