Hybrid system modeling using the SIMANLib and ARENALib Modelica libraries

Modelica is a general-purpose modeling language mainly designed to facilitate the development, reusability and exchange of models. It represents the state-of-the-art in equation-based modeling of continuous-time systems. Modelica libraries facilitate the description of multi-formalism and multi-domain models. However, the description of agent-based models (ABMs) in Modelica is not currently supported, mainly due to the characteristics of the language and its simulation algorithm. The combination of ABMs with continuous-time equations provides a powerful tool for describing and analyzing complex systems. An approach for describing ABMs using the Modelica language is presented in this manuscript, with the objective of facilitating the combination of ABMs with the rest of Modelica functionality. Agent behavior is described using a process-oriented modeling approach. Agents are described as individual entities that move across a flowchart diagram, that represents the processes that agents undergo. Processes are formally described using the Parallel DEVS formalism, extended to describe the interface with other Modelica models. The environment where agents interact is described as a cellular automaton. This approach has been implemented in a free Modelica library, named ABMLib. Three case studies are discussed to illustrate the modeling functionality of the library and its combination with other models: a basic traffic model, a sheep–wolves predator–prey model and a consumer market model.

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“…The simulation of DEVSLib models was validated and compared with equivalent models described using PowerDEVS 70 and Arena. 71…”

Section: An Implementation Of Pdevs In Modelicamentioning

confidence: 99%

“…The required outputs can be generated using custom code and using the functions provided by the Modelica Standard Library to open, write and manage files. Similarly, these functions were used in SIMANLib and ARENALib 71 to generate statistical outputs. Random actions.…”

Section: Comparison With Other Abm Toolsmentioning

confidence: 99%

Combining PDEVS and Modelica for describing agent-based models

Sanz

Urquía

2022

SIMULATION

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“…In Ref. 28, Sanz et al combined the Arena/Siman and Modelica libraries to model a heterogeneous system based on the continuous and the discrete-event model. Since Arena/Siman tool supports only the discrete-event model, the authors propose to integrate Modelica libraries to provide continuous packages as proposed by Liang et al 27 but replacing Matlab by Modelica for the continuous model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Dehghanimohammadabadi et al, 9 Discrete-event/continuous SIMIO/Matlab Manufacturing systems Integration Liang et al, 27 Discrete-event/continuous Arena/Matlab Manufacturing systems Integration Sanz et al, 28 Discrete-event/continuous Arena/Siman and Modelica Queuing systems Integration Süß et al, 29 Discrete-event/continuous PLCOpenXML, Modelica, Manufacturing systems Co-simulation and Matlab Barbieri et al, 30 Discrete-event/continuous Matlab, FlexSim, SysML, Mechatronics system Co-simulation IndustrialPhysics, and PLC Chabibi et al, 33 Discrete-event/continuous Simulink/SysML Queuing system Integration Lora et al, 35 Discrete-event Custom C/C++ Embedded system Integration Edward et al, 36 Discrete-event/continuous CyPhySim/Ptolemy II Cyber-physical system Commune Ben Ayed et al, 37 Discrete-event/continuous SystemC/Matlab/ Mechatronics system Co-simulation HW accelerator Törngren et al, 38 Discrete-event/continuous . .…”

Section: Kind Of Model Environments Target Application Techniquementioning

confidence: 99%

System-level co-simulation for embedded systems

et al. 2020

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“…A chain of events takes place when the management of an event immediately triggers another event, and so on. During the management of these chains of events, an event iteration is performed after each event and the whole model is re-evaluated during the iteration, even if the events are local to a sub-system of the discrete-event part (e.g., see case studies presented in [20] and [21]). This situation is demonstrated with an example in Section 5.…”

Section: Introductionmentioning

confidence: 99%

Improving efficiency of hybrid system simulation in Modelica

Sanz

Urquía

Casella

2014

Proceedings of the 6th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools

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Modelica supports the modeling of hybrid systems by means of differential and algebraic equations, and discrete-events. The simulation of Modelica models is carried out by solving the continuous-time equations, detecting the occurrence of events and managing the triggered events. After managing an event, the event iteration algorithm is used to find consistent restart conditions before resuming the numerical integration. The objective of the work presented in this manuscript is to describe a procedure to optimize the event iteration algorithm. The structure of the model is analyzed in order to only re-evaluate the equations involved in the management of events. The use of this procedure is described by means of an application example.

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Hybrid system modeling using the SIMANLib and ARENALib Modelica libraries

Cited by 5 publications

References 7 publications

Combining PDEVS and Modelica for describing agent-based models

Combining PDEVS and Modelica for describing agent-based models

System-level co-simulation for embedded systems

Improving efficiency of hybrid system simulation in Modelica

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