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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.
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.
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 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.
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.
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
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
The targeted experiments in our lab comprises
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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.
|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|
We offer courses in the field of control engineering and embedded systems.
|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|
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.
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.
Building H, Main building
RWU Hochschule Ravensburg-Weingarten
University of Applied Sciences
Control and Process Engineering
P.O. Box 30 22