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Control and Process Engineering

Regelungs- und Prozesstechnik
Control and Process Engineering

Control and Process Engineering

Recent engineering problems become more and more complex, nonlinear and high-dimensional. We develop together with students systems to analyze, simulate and control these technological processes. Control algorithms like Model Predictive Control and fresh high-level programming languages like Julia, Python or MATLAB are associated with artificial intelligence, embedded computing or process engineering to span a wide range of research. These fields of research are explained in detail below.

Scheme of a plate with three heat sources
Schematische Darstellung einer Platte mit Wärmequellen und Sensoren und dem dazugehörigen Temperaturverlauf.
Stephan Scholz

Thermal Process Engineering

Thermal problems play an important role in many technological processes. Many examples like building air conditioning, cooling of engines or server clusters, and optimal tempering for the production of synthetic material unveil the significance of the right heat supply. Our team develops simulations and control algorithms for thermal problems in the case of multiple heat sources. In these scenarios the controller has to decide how much heat shall be supplied and at which position. Common applications can be found in the field of bake processes for the semiconductor industry.

Scientific Machine Learning

Recent machine learning approaches have a huge impact on IT services like search engines and social networks. Though, the research of complex systems bases as well on data-driven modeling. In many cases it is too complicated to describe these complex systems in detail. Therefore, systems with partly known properties are modeled as so called grey box models to approach the real system with the computation on large data sets. For example the driving dynamics of vehicles cannot be described in detail because every component has some certain influence. Instead a grey box model is considered which is improved with data from experiments and test drives. Chemical reactions with a huge amount of particles or reactants state another example. It is very hard to state a model in closed form for these reactions because every component could interact with each other. Thus, these chemical reactions are modeled as grey box models and optimized with data from experiments.

See also: SciML

Embedded Systems

Embedded systems can be found in almost every technological application ranging from consumer electronics to industrial robotics. They are used for measuring and processing data, for example light management in buildings. In many cases, commercial controllers are less flexible and more expensive than single-board computer. Thus, we use single-board computers as controlling units in scientific projects and small scale industrial processes in the areas of process technology, electrical and mechanical engineering to examine their field of application.

Projects and Theses

Our projects and theses cover the research topics: thermal process engineering, scientific machine learning and embedded systems. Interested students can choose either software related topics with a solid theoretical basis or hardware and lab related topics including experiments and their evaluation.

Software development

We develop mathematical models and controllers for complex dynamical systems and implement them in modern and common programming languages like Julia, MATLAB, Python or C/C++. Projects and theses are offered in the areas of

Lab related topics

We work on the transformation from closed source and proprietary hard- and software towards open source solutions based on platforms like Raspberry Pi or Arduino. Our offered projects and theses cover all topics of systems and control engineering as

  • design and construction of hardware,
  • modeling and system identification of the plants and
  • implementation of controllers and user interfaces.

The targeted experiments in our lab comprises

  • Electrical and mechanical oscillators,
  • Hydraulic models (water tank, heel control) and
  • Inverse Pendulums.

The Embedded Control seminar shall enable the students to delve into a topic of recent research and development and to present their results to an audience. Available topics cover various branches of control engineering like Nonlinear Control or related fields like Mathematical Optimization, Numerical Analysis or Machine Learning.

Recent Topics
Mathematical Optimization Fundamentals and Automatic Differentiation
Robust and nonlinear control H-infinity control and Port-Hamiltonian Systems
Applications of the Kalman filter LQG control and Ensemble, Extended and Unscented Kalman filter
Data driven modeling and control Time series, ARMA models and Koopman Operator Theory
Scientific Machine Learning Symbolic-numeric computation und Continuous Normalizing Flows


Presentation of Seminar Topics
Day Time Topic
22.01.2021 11:30
  1. Optimization problems
  2. Automatic Differentiation
  1. H-infinity - Optimizing robust performance and robust stabilization
  2. Ensemble Kalman filter - Monte Carlo approximation
29.01.2021 11:30
  1. Extended and Unscented Kalman filters - Nonlinear observers
  2. Scientific Machine Learning - ModelingToolkit.jl
  1. Noise driven systems - Wiener process
  2. Port-Hamiltonian Systems
05.02.2021 11:30
  1. Data driven modeling - Time series, autoregressive, moving-average
  2. Data-Driven Control - Koopman Theory
  1. Linear–quadratic–Gaussian control - Kalman filter as an observer
  2. Neural ODE - Continuous normalizing flows

We offer courses in the field of control engineering and embedded systems.

List of courses

Microcontroller Winter / Summer Lecture and Lab Moodle course
Control Engineering Winter / Summer Lecture and Lab Moodle course
Advanced Control Systems: Digital Control Summer only Lecture and Lab Moodle course
Embedded Control Winter only Seminar and Lab Moodle course
Tutorial Control Engineering (optional) Winter / Summer   Moodle course


Control Engineering

This course gives an introduction to linear continuous and digital control systems in the time and frequency domain. It covers the modeling of dynamical systems as ordinary differential equations and transfer function and the design of controllers with heuristic, algebraic and graphic methods.


  1. Modeling of dynamical systems in the time and frequency domain
    • Laplace Transform
    • Transfer functions
    • Block diagrams
  2. Analysis of open- and closed-loop linear systems
    • Proportional, integral and differential (PID) behavior
    • Bounded-input-bounded-output (BIBO) stability
    • Bode and Nyquist diagram
  3. Algebraic, graphic and heuristic controller design methods
    • Routh-Hurwitz criterion
    • Nyquist criterion
    • Root locus analysis
    • Ziegler-Nichols and Naslin method
  4. Digital control systems in the frequency domain
    • Z-Transform
    • Zero-Order Hold
    • Bilinear transform (Tustin’s method)
    • Jury stability criterion
    • Dead-beat control

Advanced Control Systems

Modern complex dynamical systems are modeled, simulated and controlled in the time domain as presented in this course. In particular, common state-space methods for the analysis and control of linear time-invariant system are discussed and the modeling and control of nonlinear systems are introduced.


  1. Modeling of dynamical systems in the state-space
    • Laplace transform
    • Eigenvalues and Eigenvectors
    • Stability of LTI systems
  2. Design of feedback systems
    • Controllability
    • Full State feedback
    • Ackermann's formula
    • MIMO systems via Eigenvalue decomposition
  3. Design of state observers
    • Observability
    • Luenberger State Observer
    • Separation principle
  4. Introduction to optimal control
    • Linear-Quadratic Regulator
  5. Nonlinear systems and discretization methods
    • Zero-Order Hold
    • Lyapunov Stability
    • Van-der-Pol Oscillator
  6. Introduction to nonlinear control systems
    • Sliding-Mode Control
    • Nonlinear Model Predictive Control

Contact & People

General contact details

Room H240
On campus
Building H, Main building
88250 Weingarten
Postal address RWU Hochschule Ravensburg-Weingarten
University of Applied Sciences
Control and Process Engineering
P.O. Box 30 22
88216 Weingarten

Lab Team

Prof. Dr.-Ing. Lothar Berger

Leiter des Labors Regelungs- und Prozesstechnik
Regelungstechnik, Prozesstechnik, Mathematische Methoden
Lothar Berger

Stephan Scholz M.Sc.

Akademischer Mitarbeiter, Doktorand
Modellbildung, Numerische Simulation, Regelungstechnik
Stephan Scholz

Dipl.-Ing.(FH) Jürgen Sonntag

Wissenschaftlicher Mitarbeiter der Studiengänge Elektromobilität und Regenerative Energien, Elektrotechnik und Informationstechnik & Masterstudiengang Electrical Engineering
Regelungstechnik, Automatisierungstechnik