Interoperability based Dynamic Data Mediation using Adaptive Multi-Agent Systems for Co-Simulation

A co-simulation is the coupling of several simulation tools where each one handles part of a modular problem which allows each designer to interact with the complex system in order to retain its business expertise and continue to use its own digital tools. For this co-simulation to work, the ability to exchange data between the tools in meaningful ways, known as Interoperability, is required. This paper describes the design of such interoperability based on the FMI (Functional Mock up Interface) standard and a dynamic data mediation using adaptive multi-agent systems for a co-simulation. It is currently being applied in neOCampus, the ambient campus of the University of Toulouse III - Paul Sabatier

WIP on a Coordination Language to Automate the Generation of Co-Simulations

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Cyber-physical energy systems modeling, test specification, and co-simulation based testing

The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification

A PVS-Simulink Integrated Environment for Model-Based Analysis of Cyber-Physical Systems

This paper presents a methodology, with supporting tool, for formal modeling and analysis of software components in cyber-physical systems. Using our approach, developers can integrate a simulation of logic-based specifications of software components and Simulink models of continuous processes. The integrated simulation is useful to validate the characteristics of discrete system components early in the development process. The same logic-based specifications can also be formally verified using the Prototype Verification System (PVS), to gain additional confidence that the software design complies with specific safety requirements. Modeling patterns are defined for generating the logic-based specifications from the more familiar automata-based formalism. The ultimate aim of this work is to facilitate the introduction of formal verification technologies in the software development process of cyber-physical systems, which typically requires the integrated use of different formalisms and tools. A case study from the medical domain is used to illustrate the approach. A PVS model of a pacemaker is interfaced with a Simulink model of the human heart. The overall cyber-physical system is co-simulated to validate design requirements through exploration of relevant test scenarios. Formal verification with the PVS theorem prover is demonstrated for the pacemaker model for specific safety aspects of the pacemaker design

MECSYCO: a Multi-agent DEVS Wrapping Platform for the Co-simulation of Complex Systems

Most modeling and simulation (M&S) questions about complex systems require to take simultaneously account of several points of view. Phenomena evolving at different scales and at different levels of resolution have to be considered. Moreover, expert skills belonging to different scientific fields are needed. The challenges are then to reconcile these heterogeneous points of view, and to integrate each domain tools (formalisms and simulation software) within the rigorous framework of the M&S process. To answer to this issue, we propose here the specifications of the MECSYCO co-simulation middleware. MECSYCO relies on the universality of the DEVS formalism in order to integrate models written in different formalism. This integration is based on a wrapping strategy in order to make models implemented in different simulation software inter-operable. The middleware performs the co-simulation in a parallel, decentralized and distributable fashion thanks to its modular multi-agent architecture. We detail how MECSYCO perform hybrid co-simulations by integrating in a generic way already implemented continuous models thanks to the FMI standard, the DEV&DESS formalism and the QSS method. The DEVS wrapping of FMI that we propose is not restricted to MECSYCO but can be performed in any DEVS-based platform. We show the modularity and the genericity of our approach through an iterative smart heating system M&S. Compared to other works in the literature, our proposition is generic thanks to the strong foundation of DEVS and the unifying features of the FMI standard, while being fully specified from the concepts to their implementations

Design of mechatronic systems through aspect and object-oriented modeling

Design of mechatronic systems involves the use of multiple disciplines, from mechanics to electronics and computer science. Different granularities of hybrid co-simulations with increasing details can be used during the design process. However, there is the need of modeling tools for effectively managing the necessary abstraction layers. This work proposes a combination of Aspect-Oriented and Object-Oriented modeling for reaching the goal. Moreover, it shows how the utilization of these tools can facilitate design-space exploration, segregation of domains of expertise and enhances co-design

Modeling and Simulation Methodologies for Digital Twin in Industry 4.0

The concept of Industry 4.0 represents an innovative vision of what will be the factory of the future. The principles of this new paradigm are based on interoperability and data exchange between dierent industrial equipment. In this context, Cyber- Physical Systems (CPSs) cover one of the main roles in this revolution. The combination of models and the integration of real data coming from the field allows to obtain the virtual copy of the real plant, also called Digital Twin. The entire factory can be seen as a set of CPSs and the resulting system is also called Cyber-Physical Production System (CPPS). This CPPS represents the Digital Twin of the factory with which it would be possible analyze the real factory. The interoperability between the real industrial equipment and the Digital Twin allows to make predictions concerning the quality of the products. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. Several tools [2] allow to model a production line, considering dierent aspects of the factory (i.e. geometrical properties, the information flows etc.) However, these simulators do not provide natively any solution for the design integration of CPSs, making impossible to have precise analysis concerning the real factory. Furthermore, for the best of our knowledge, there are no solution regarding a clear integration of data coming from real equipment into CPS models that composes the entire production line. In this context, the goal of this thesis aims to define an unified methodology to design and simulate the Digital Twin of a plant, integrating data coming from real equipment. In detail, the presented methodologies focus mainly on: integration of heterogeneous models in production line simulators; Integration of heterogeneous models with ad-hoc simulation strategies; Multi-level simulation approach of CPS and integration of real data coming from sensors into models. All the presented contributions produce an environment that allows to perform simulation of the plant based not only on synthetic data, but also on real data coming from equipments