Course content

Methods of Engineering Cybernetics covers vital elements of the basic theory needed to study engineering cybernetics at the graduate level.

The course consists of three modules:

Simulation:

Simulation of linear, time-varying and nonlinear differential equations, homogenous and inhomogeneous. Temporal discretisation techniques are discussed and implemented in appropriate software tools. Methods of stability analysis are mentioned.

Modeling:

Modeling of mechanical systems using Lagrangian methods will be discussed. Simple kinematics for vehicles and manipulators are covered, along with the notion of rotation matrices, translations and the necessary rudimentary group theory. Simple electrical circuit analysis is introduced.

Optimization:

Techniques and theory for solving simple optimization problems are covered, centered on linear-quadratic problems. Analytical and numerical techniques for least-squares optimization are discussed, as well as basic regularization techniques. Tools for dealing with constraints are mentioned.

Learning outcome

General:

Acquire the basis for advanced courses in engineering cybernetics. Communicate about the contents and applications of the course with specialists and a general audience. Be able to reflect about the utilisation of cybernetics, and the potential of this field of study, in production, infrastructure and society.

Simulation:

Knowledge:

Overall understanding of differential equations and how to simulate them. Shall be able to select an appropriate simulation algorithm, set the duration of time steps, and understand benefits and limitations in popular methods.

Skill:

Be able to program and execute simple simulation algorithms, such as Euler's explicit or implicit method, in a suitable software tool. Can perform simulation studies and interpret the result qualitatively.

Modeling:

Knowledge:

Understand the basic principles of Lagrangian mechanics and its applications. Can posit simple kinematical equations for robots and vehicles with few degrees of freedom. Basic understanding of rotation matrices, their parametrisation and their incorporation in a simulation. Will understand Ohm's law, Kirchhoff's laws as well as the mathematical description of inductors and capacitors. Can perform simple circuit analysis with linear algebra.

Skill:

Formulation and simulation of simple problems in robotics, with suitable software tools. Can formulate simple circuit analysis problems and solve them numerically.

Optimization:

Knowledge:

Understand the applications of least-squares methods, and have an overview of alternative methods. Shall be able to formulate and solve simple linear-quadratic optimization problem.

Skill:

Be able to program simple optimization algorithms for curve-fitting, parameter estimation and allocation.

Learning methods and activities

Lectures and a number of compulsory exercises.

Compulsory assignments

• Exercises

Required previous knowledge

Mathematics corresponding to a B.Sc. in engineering.

Course materials

Textbook and lecture notes, announced on the learning management system before start of semester.