The "Control Engineering" module consists of 3 module components:
- Fundamentals of Control Engineering,
- Digital Control Engineering,
- Control Engineering Laboratory

These concepts are explained in both the time domain and the frequency domain so that they can be applied to the analysis of linear time-invariant systems in control engineering.
The focus is on the analysis of technical systems in the frequency domain and the synthesis of closed-loop control systems using common control algorithms. Controller design is carried out using analytical and graphical methods such as the root locus method, the Nyquist plot, and the Bode plot. Simple optimization methods for control loops are also introduced.
At the end of the module, a brief overview of the state-space description methodology and associated solution methods using the matrix-e function is provided.
Control engineering simulation tools are used to support the module.
As part of the “Control Engineering Laboratory” coursework, students work in groups to solve a problem from the field of control engineering in the laboratory. The module concludes with a Graded Assessment covering both the “Fundamentals of Control Engineering” and “Digital Control Engineering” components.

The module “Introduction to Electrical Engineering I”
of the international master’s program “M.Sc. Mechatronics” includes the following module components:
- Electrical Engineering and
- Linear Control

The module component “Linear Control” presented here covers the fundamentals of linear control theory in the frequency domain and, building on this, introduces students to system description in state space (time domain).
Frequency-domain methods focus on the description of technical systems using linear, time-invariant transfer functions, as well as the associated controller synthesis using common closed-loop control algorithms. The root locus method, Nyquist plots, and Bode plots are used for controller design.
In the subsequent description of systems in state space, the mathematical fundamentals are taught using differential equations, and normal forms are introduced. This is then used to explain the design of state-space control systems.
In the “Linear Control” module, control engineering simulation tools are used to support the learning process. Laboratory experiments are offered as an option. The “Linear Control” module concludes with a Graded Assessment.

The “Mechatronic Systems” module begins by providing an overview of the components of mechatronic systems and introduces common mechatronic applications.
It then focuses on stationary robot systems as exemplary standard mechatronic systems. To this end, it covers aspects of the kinematics of rigid bodies and open kinematic chains, as well as the coordinate transformations required to describe movements in the three-dimensional workspace of a stationary robot.
Building on this, methods for deriving dynamic models—as required for controlling robot movements—are presented.
The module then addresses drive concepts typical of robots and introduces the necessary actuators (motors and gearboxes).
The control loop is completed by a description of the internal sensor systems required to measure the current positions, velocities, and forces/moments acting on the robot axes.
The lecture is supplemented by appropriate exercises related to the theoretical material.
As part of the coursework, students in this module work in groups to solve a problem in the field of robotics in the laboratory.
The module concludes with a Graded Assessment.

The module “Optimal and Adaptive Control of Linear and Nonlinear Systems” in the master’s program in Electrical Engineering provides an overview of the analysis of nonlinear control systems and introduces methods for their control. These are supplemented by discussions on the adaptive control of processes and the design of optimal controllers.
In reality, nonlinear systems are common. Building on the linear methods covered in the B.Sc. module “Fundamentals of Control Engineering,” this course examines more complex nonlinear systems. First, systems are analyzed in state space; subsequently, controller design methods are presented in the phase plane. Methods covered include the harmonic balance method, control using 2- and 3-point characteristic curves, and stability analyses according to Lyapunov and Popov.
Building on this foundation, general optimization methods in control engineering are examined. Evaluation and controller design criteria are discussed. Key methods include the Lagrange optimization approach, solution methods by Euler, Lagrange, and Hamilton, as well as Pontryagin’s maximum principle. Dynamic programming is taught using Bellman’s algorithm. In adaptive control, the methods “gain scheduling,” “self-tuning,” and “model reference control” are introduced.
The theoretical portion of the module is supported by relevant exercises and simulations.
The module includes laboratory experiments on nonlinear control theory as part of the Coursework requirements. The module concludes with a Graded Assessment (written/oral, as announced in advance).
Note: The lecture and exercise sessions for this module are conducted online; the associated lab sessions take place in person.

The "Driver Assistance Systems" module covers the fundamentals needed to understand driver assistance systems.
The following topics are covered:

• Vehicle handling, driving safety, active and passive systems
• Tire characteristics, braking processes, anti-lock braking systems (ABS), traction control (ASR)
• Electronic Stability Program (ESP)
• Automatic braking functions (e.g., HHC), electrohydraulic brakes (SBC), electromechanical brakes (EMB)
• Adaptive Cruise Control (ACC)
• Lane-keeping and lane-changing assistants, active steering
• Occupant protection systems
• Parking assist, vehicle lighting
• Vehicle information systems, navigation
• Automated Driving

The Driver Assistance Systems module provides the fundamentals for developing simulations in the field of driver assistance systems.
The exercise covers

• Vehicle dynamics modeling
• Simulations to verify the operation of multiple
  driver assistance systems

The module concludes with a Graded Assessment.

The "Advanced Driver Assistance Systems" module teaches the fundamentals needed to understand driver assistance systems.
The following topics are covered:

• Vehicle handling, driving safety, active and passive systems
• Tire characteristics, braking processes, anti-lock braking systems (ABS), traction control (ASR)
• Electronic Stability Program (ESP)
• Automatic braking functions (e.g., HHC), electrohydraulic brakes (SBC), electromechanical brakes (EMB)
• Adaptive Cruise Control (ACC)
• Lane-keeping and lane-changing assistants, active steering
• Occupant protection systems
• Parking assist, vehicle lighting
• In-vehicle information systems, navigation
• Automated Driving

The Driver Assistance Systems module provides the fundamentals for developing simulations in the field of driver assistance systems.
The exercise covers

• Vehicle dynamics modeling
• Simulations to verify the operation of multiple
  driver assistance systems

The module concludes with a Graded Assessment.

The “Control Engineering” module consists of 3 module components:
- Fundamentals of Control Engineering,
- Digital Control Systems, -Control Systems Laboratory

The lecture focuses on digital control systems. The prerequisites and design methods for digital controllers are examined. The methods covered include the z-transform, quasi-continuous controller design, the description of digital control loops, classical digital controllers, and dead-beat controllers.
As part of the “Control Systems Laboratory” coursework, students work in groups to solve a problem from the field of control systems in the laboratory. The module concludes with a Graded Assessment that covers both the “Fundamentals of Control Engineering” and “Digital Control Engineering” components.

The module “Introduction to Control Engineering for Computer Scientists” explains the relationships between signals in the time domain and the frequency domain, with the aim of applying them to the analysis of linear time-invariant systems in control engineering.
The focus is on the analysis of technical systems in the frequency domain and the synthesis of closed-loop control systems using common control algorithms. Controller design is carried out using analytical and graphical methods such as the root locus method, the Nyquist plot, and the Bode plot. Simple optimization methods for control loops are also introduced.
The module concludes with a Graded Assessment.

This required module of the M.Sc. in Electrical Engineering introduces the description of dynamic systems in the time domain by deriving the corresponding state equations in common normal forms. Solution methods are explained using the matrix-e function.
Building on this, the calculation and synthesis of state controllers via pole placement are described, and state estimation using deterministic observers is introduced.
Subsequently, the theory of decoupling multi-variable systems and associated design methods are presented. In addition, the module outlines methods for describing nonlinear systems and their decoupling.
The module also provides insights into the theory of the LQ controller and the Kalman filter.
Control engineering simulation tools are used to support the material in this module. The module concludes with a Graded Assessment.

The “Programming Lab” module provides students with a solid understanding and knowledge of the practical implementation of various aspects of microcontroller programming and drive control. Students also learn how to read data from various sensors and evaluate it using algorithms they have developed themselves. After completing the “Programming Laboratory Practicum,” students will be able to interconnect, control, and regulate various hardware components using their own programming elements.
Prior completion of the course “Algorithms and Data Structures for Electrical Engineers” is a prerequisite for this lab practicum. The lab practicum begins with a brief introduction (covering the development environment and hardware, as well as the methods to be used). Afterward, students work in groups to complete the assigned tasks through self-study. Progress is discussed and documented in regular meetings with the instructors. The lab course counts as coursework toward the B.Sc. program in Electrical Engineering.

In this interdisciplinary internship, students gain the necessary technical expertise in the field of automation and energy engineering at the master’s level and develop methodological skills in applying this knowledge.
Students will be equipped to analyze complex problems in the field of automation and energy engineering, apply the theoretical knowledge gained in lectures to practical situations, and select and apply appropriate methods for testing and verifying solutions.
The internship includes laboratory experiments from the following departments:
- Reliability of Technical Systems and Electrical Measurement Technology, - Power Electronics and Electric Drives, - Control Engineering and Autonomous Robotics (RST), and - Electrical Machines, Drives, and Control Systems.
The laboratory practicum counts as coursework in the M.Sc. program in Electrical Engineering.

The technical content of the individual seminar paper in the fields of control engineering, automation technology, and robotics is coordinated with the instructors. These topics are secondary to the targeted methodological skills (literature research and its summary/synthesis) and key competencies (preparation and delivery of a presentation to an audience) and may, where appropriate, prepare students for and complement a desired focus for their term papers and theses.
The seminar constitutes coursework in the B.Sc. program in Electrical Engineering (FPO 2012).