How can the dynamic behaviour of a mechanical mass-spring-damper system be similar to an electrical resistance-capacitance-inductance circuit? Motivated by this question, this course introduces the signals – systems framework that helps in describing the dynamic behaviour of systems for a variety of inputs (signals). Useful analysis tools both in the frequency- and the time-domain are also introduced. In the later part of the course, these concepts will be used to understand basic signal processing in the form of both analogue and digital filter design.

This is the second course in control engineering which looks at the state-space representation of systems as well as state-space based control design techniques. The course also introduces basic concepts in System Identification and Nonlinear Control. Traditional continuous-time as well as sampled-data (digital) systems are covered.

The aim of the course is to give an overview of the key techniques for enabling mobile robots to localise themselves, map their environments or do both simultaneously. The course introduces students to the fundamentals of statistical modelling and state estimation, widely used in automated vehicles and industrial automation.

Mobile robots can be used in a range of applications, including warehouses, agriculture, and other real-world environments. One of the main challenges for robots operating in the real world is that this is an unstructured environment. Nature has found clever solutions for the design of intelligent and effective systems operating in the unstructured environment hence biology is an obvious source of inspiration for robotics. In this course we take inspiration from nature to engineer intelligent systems for real-world applications as, for example, locomotion.

Ever wondered how Excel is able to draw an optimal line through a set of points? This course looks at how typical engineering problems that cannot be described mathematically (or are difficult to do so) can be solved so that the optimal solution is found. The course contains a range of examples to show how the techniques are applied to real world problems in different engineering disciplines. The course will show how to develop computational algorithms from scratch, with a fundamental understanding of how the algorithms function, both mathematically and then in real time on a computer.

Robotics is an essential component of Industry 4.0. The adoption of robots in industries worldwide is on the rise and robotic arms are the most successful robotic platform.

The course introduces students to the analysis and use of robot arms, by exposing them to the theoretical basis of robotics as well as their practical implementation. This course focuses on the kinematics, dynamics and control of robotic arms.

The aim of the course is to give an overview of the different approaches to specify motions of industrial and mobile robots, up to autonomous robots that learn from their experiences. The course introduces students to the fundamentals of machine learning, which are relevant for robotics research and practice.