On the difficulities of real-time co-simulation

In a co-simulation, subsystems are coupled via their in- and outputs to simulate the overall system behaviour. The subsystems are modelled in their domain specfic simulation tools. The task changes if one coupled subsystem represents a real-time system. A real-time system which has to guarantees hard-real-time conditions influences the co-simulation concept: now the co-simulation also has to fulfill hard-real-time conditions. This type of co-simulation is called real-time co-simulation. The most important difference to a non-real-time co-simulation is the time correct overall simulation speed with respect to the involved real-time systems. To achieve this, all subsystems in form of non-real-time systems have to be synchronised to the involved real-time systems. The focus of this work lies on the problems that occur in a real-time co-simulation environment compared to a classical one. A concept to handle the additional problems is outlined and tested on an example real-time co-simulation

Modelování a identifikace modelu magnetické levitace CE 152 / revidovaný

Paper describes procedure of first principle modelling and experimental identification of Magnetic Levitation Model CE 152. Author optimized and simplified dynamical model to a minimum what is needed to characterize given system for the simulation and control design purposes. Only few experiments are needed to estimate the unknown parameters. Model quality is verified in the feedback control loop where the real and simulated data are compared. © 2019, Springer International Publishing AG, part of Springer Nature.Článek popisuje postup fyzikálního modelování a experimentální identifikace magnetického modelu levitace CE 152

Recommender Systems for Configuration Knowledge Engineering ∗

The knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and maintenance processes. We discuss a couple of scenarios for the application of recommender systems in knowledge engineering and report the results of empirical studies which show the importance of user-centered configuration knowledge organization.

PRYSTINE - Technical Progress after Year 1

Among the actual trends that will affect society in the coming years, autonomous driving stands out as having the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches also due to missing reliable environment perception and sensor fusion. PRYSTINE will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. In this paper, we detail the vision of the PRYSTINE project and we showcase the results achieved during the first year

Programmable Systems for Intelligence in Automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3

Programmable systems for intelligence in automobiles (PRYSTINE): Final results after Year 3

Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches. The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3