Over the last 40 years, a considerable number of formalisms and simulators have been developed for modeling and simulating Discrete Event Systems (DES). One formalism that has received particular attention in the research community is the Discrete Event System Specification (DEVS). This approach, today known as Classic DEVS, was developed by Zeigler from systems theory and published in 1976. Over the years, there have been further developments of the formalism with respect to specific application needs, such as fuzzy, hybrid or real-time simulations. The main evolution for general use is Parallel DEVS (PDEVS), which was introduced by Chow in 1994. Classic DEVS as well as PDEVS can be viewed primarily as extensions of a Moore machine. Therefore, modeling of certain Mealy-type components can be difficult with these formalisms. Preyser et al. analyzed this problem and presented a revised version RPDEVS in 2018. In 2020 Junglas showed that RPDEVS still has problems with complex structures of concurrent events. He proposed the use of concepts of Nonstandard Analysis to develop a more robust method of handling concurrent events. The short name of the new formalism variant should be NSA-DEVS.

Subsequently, this idea was further developed as joint research with the CEA group. Moreover, NSA-DEVS became a central component in this cooperative PhD project with the Institute for Automation at the University of Rostock.

Manufactural assembly is becoming increasingly complex. At the same time, efforts to increase efficiency are on the rise. Among other things assembly processes where humans and robots work together offer great potential for improvement.

Aim of the project is to enable the robot control to independently learn a behaviour strategy to react on the stochastic behaviour of humans and the entire environment with the help of Reinforcement Learning.

It is investigated how a robot control can be generated from a learned behaviour strategy, how reinforcement learning can be accelerated, and how the learning effort of the learning process can be reduced by using several agents.

Modeling and Simulation (M&S) forms the basis of the modern development and planning
process in engineering. Modeling deals with the abstraction of essential aspects of technical
systems and their implementation in a model. During simulation the implemented model is
executed aiming to gain knowledge about the system behavior. A single model execution is
called a simulation run. A simple simulation experiment comprises many simulation runs
with varied input variables. Complex simulation-based experiments are characterized by the
integration of simulation with numerical methods such as optimization, screening or sensi-
tivity analysis. Highly complex simulation experiments investigate the different model
structures in addition to the parameter space.
The classical M&S approach is increasingly reaching its limits in engineering. One
reason is the growing customers demand for customized products. This increases the variety
and often the complexity of technical systems as well as of the resulting models and
investigation objectives. This means that the specification and execution of simulation
experiments includes not only a set of model variants with their parameter settings, but also
combinations with numerical methods and their configuration. Furthermore, the sequence of
variants to be investigated or numerical methods to be applied often arise from previously
determined experiment results.
Accordingly, this work contributes to the development of general methods for variant
management in M&S up to the simulation experiments’ level and their automated execution.
The proposed solutions are developed based on the ASIM procedure model, the modular-
hierarchical modeling approach and the System Entity Structure/Model Base (SES/MB)
framework. The variant management is based on different phases: variant analysis,
variant formalization, variant implementation and variant generation. Furthermore, a
modular-hierarchical structuring for simulation experiments is developed. By extending the
SES/MB framework, not only model variants but complete experiment variants can be speci-
fied, implemented and generated. Moreover, a framework for automated experimentation
based on the extended SES/MB framework is proposed. Finally, the methods are presented
using an application example from the field of production and logistics.
thesis

In practical control systems, deadzone characteristics are encountered in a wide range of mechanical and electrical components, such as valves or DC servo motors. Another process that exhibits a deadzone characteristic is the three-way catalyst. Due to its oxygen storage ability, the normalized air-fuel ratio post catalyst remains at one despite air-fuel mixture variations pre catalyst, as long as certain levels of the oxygen storage state are not exceeded. 
The aim of this project is to reduce the exhaust emissions produced by cars equipped with a three-way catalyst by improving the control of the air-fuel mixture. Low emissions are reached if post-catalyst air-fuel ratio remains at one despite large air-fuel mixture variations and a biased measurement of pre-catalyst air-fuel ratio. Typically, the aim of a controller for deadband processes is to overcome the deadzone in an optimal way, commonly by employing the deadzone inverse to cancel it. For this project, however, the aim of the controller is to keep the oxygen storage state within the deadzone. 
In order to control the oxygen storage state (and pre-catalyst air-fuel ratio which is connected to the storage state via the deadzone), a model-based approach is proposed, since the storage state cannot be measured directly by a sensor. Thus, the project includes i) the development of a control-oriented model of a three-way catalyst, which has to be both accurate and simple enough to be calculated on the vehicles electronic control unit, and ii) the design of a suitable control strategy. 

Due to increasing demands on driving comfort, consumption and emission of modern combustion engines new combustion processes came to introduction of series production in the last years. The potential of these combustion processes in general can only be utilized by introduction of additional actuating and measure variables. Thereby an increasing complexity from control viewpoint is almost unavoidable. The choice of particular actuating variables has decisive influence on efficiency, comfort and waste gas emission. Besides the main manipulated variables, as e.g. the throttle position, there are some more different actuating variables that are in many cases only effective for limited operating ranges of the engine with different effects.
The cooperative graduation project deals with the general problem of control with auxiliary actuating and control variables. Application emphasis of the procedures to be developed is located in the field of combustion engine controllers.
One aim is to find the best compromise between efficiency, driving comfort and waste gas emission. This is achieved by an optimized choice, according to several criteria, of available actuating variables for the realization of the driving torque. 
thesis

The coordination of available actuators in modern engine control units (ECUs) is a challenging task. The broad variety of signals (e.g. throttle, advance angle, exhaust gas recirculation, injection, etc.) to influence the momentum and engine speed are coupled. Therefore a multi-variable control should manage these actuators to fulfill the control aim in an efficient manner. A compromise respecting further aims like comfort issues and exhaust gas emissions must be found. An available scheme to cope with these requirements is model predictive control (MPC). The incorporated optimization ensures the optimal selection of actuator signals under their constraints. 
The cooperative graduation project is concerned with the development of a suitable quadratic program that solves the optimization within the MPC algorithm. It needs to fulfill the real-time requirements when run on high-potential micro-controllers (e.g. Infineon Tricore) that are incorporated in modern ECUs. 

Progressive robotics research opens up new application fields incessantly. Hence, demands on the development of robot controls are increasing. Easy programming and integration of different external hardware are of particular importance. The aim of every control implementation is to realise easy, safe, fast and cost-effective design and commissioning of robot applications. Therefore, it is essential to avoid re-implementations in the entire development process. 
An approach for integrated modeling, simulation and operation, named "simulation model-based rapid control prototyping", is introduced and illustrated by the example of a sensor based robot control. It is discussed how simulation models have to be structured in the early system design stage in order to extend them to model-based control programs for the operation stage stepwise. 
Objects of this research are (i) the prevention of re-implementations, (ii) the development of a task oriented programming approach, (iii) methods for an easy integration of different hardware and (iv) a concept for specification of high flexible and re-configurable controls. 
thesis