Modeling and simulation of the two-tank system within a hybrid framework

Most real-world dynamical systems are often involving continuous behaviors and discrete events, in this case, they are called hybrid dynamical systems (HDSs). To properly model this kind of systems, it is necessary to consider both the continuous and the discrete aspects of its dynamics. In this paper, a modeling framework based on the hybrid automata (HA) approach is proposed. This hybrid modeling framework allows combining the multi-state models of the system, described by nonlinear differential equations, with the system’s discrete dynamics described by finite state machines. To attest to the efficiency of the proposed modeling framework, its application to a two-tank hybrid system (TTHS) is presented. The TTHS studied is a typical benchmark for HDSs with four operating modes. The MATLAB Simulink and Stateflow tools are used to implement and simulate the hybrid model of the TTHS. Different simulations results demonstrate the efficiency of the proposed modeling framework, which allows us to appropriately have a complete model of an HDS

Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems

This open access book coherently gathers well-founded information on the fundamentals of and formalisms for modelling cyber-physical systems (CPS). Highlighting the cross-disciplinary nature of CPS modelling, it also serves as a bridge for anyone entering CPS from related areas of computer science or engineering. Truly complex, engineered systems—known as cyber-physical systems—that integrate physical, software, and network aspects are now on the rise. However, there is no unifying theory nor systematic design methods, techniques or tools for these systems. Individual (mechanical, electrical, network or software) engineering disciplines only offer partial solutions. A technique known as Multi-Paradigm Modelling has recently emerged suggesting to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s), and then weaving the results together to form a representation of the system. If properly applied, it enables, among other global aspects, performance analysis, exhaustive simulation, and verification. This book is the first systematic attempt to bring together these formalisms for anyone starting in the field of CPS who seeks solid modelling foundations and a comprehensive introduction to the distinct existing techniques that are multi-paradigmatic. Though chiefly intended for master and post-graduate level students in computer science and engineering, it can also be used as a reference text for practitioners

Integrierte modell- und simulationsbasierte Entwicklung zur dynamischen Bewertung automobiler Elektrik/Elektronik-Architekturen

Die Automobilbranche befindet sich seit einigen Jahren im Wandel. Trends wie autonomes Fahren, Konnektivität, smarte Mobilität sowie die Elektrifizierung führen zu einer drastischen Erhöhung der Fahrzeugkomplexität. Diese Komplexität muss durch die zugrunde liegende Elektrik/Elektronik-Architektur (E/E-Architektur) beherrscht werden und ruft unmittelbare neue Herausforderungen an den Entwicklungsprozess hervor. Design-Entscheidungen der E/E-Architektur haben maßgeblichen Einfluss auf das Verhalten von Fahrzeugfunktionen und umgekehrt. Daher müssen sie möglichst frühzeitig analysiert und evaluiert werden, um kostspielige Fehlerkorrekturen in späten Entwicklungsphasen zu minimieren. Eine frühzeitige Einbindung von Simulationsmethoden ist dabei zentral. Die modellbasierte Architekturentwicklung und Simulation sind jedoch weitestgehend getrennt voneinander laufende Prozesse. Dies erschwert eine effiziente Analyse sowie Bewertung der bidirektionalen Abhängigkeiten zwischen Architektur und Verhalten. Um diese Schwächen zu adressieren, wird in dieser Arbeit eine integrierte Methodik zur modell- und simulationsbasierten Entwicklung von E/E-Architekturen vorgestellt, die sich in drei Teile gliedert. Es werden zunächst neue Methoden zur architekturzentrierten Verhaltensmodellierung eingeführt. Eine nachfolgende Synthese generiert daraus ein Simulationsmodell, welches automatisiert mehrere Abstraktionsebenen der E/E-Architektur miteinander verknüpft und so zu einer ganzheitlichen Betrachtung beiträgt. Mithilfe des integrierten Ansatzes wird zusätzlich ein Konzept entwickelt, das es gestattet, mehrere Architekturvarianten automatisiert bzgl. statischen und dynamischen Metriken gegenüberzustellen. Die Konzepte werden in das in der Automobilindustrie etablierte E/E-Architekturwerkzeug PREEvision® integriert, umgesetzt und anhand mehrerer Anwendungsfälle evaluiert

Model to code generation of UML/SysML activity diagrams for ARM CortexM MCUs

The complexity in embedded systems has been increased in the last years. To overcome the system complexity various methodologies have been presented. Both in industry and academia, Model-Based design has been accepted to be the best solution to solve this problem. Model-Based Design is a technique for developing embedded system applications and cyber-physical systems based on a hierarchy of reusable design blocks. SysML/UML activity diagrams are widely used for the modelling and analysis of complex systems, and they have become the de facto standard for software and embedded systems. Previously in our group, we formalized SysML activity diagrams by developing a calculus called New Activity Calculus (NuAC). In this work, we redefined NuAC terms to support RTX (Keil Real-Time Operating System) and present an automated SysML/UML activity diagram to RTX code generator, using mapping rules expressed in NuAC. To achieve this goal, we proposed a set of mapping rules that were used in mapping a SysML/ UML activity diagram into a suitable code to be executed on ARM CortexM processor family. To automate the process of code generation, we presented a JAVA application that uses the proposed rules to automatically generate the RTX code from the input activity diagram model. To demonstrate the capability of the developed tool, we use it to implement a train control algorithm on an ARM Cortex-M4 device. The implemented model is run on the target platform and the correct functionality of the system is verified