157 research outputs found
Hybrid Simulation Safety: Limbos and Zero Crossings
Physical systems can be naturally modeled by combining continuous and
discrete models. Such hybrid models may simplify the modeling task of complex
system, as well as increase simulation performance. Moreover, modern simulation
engines can often efficiently generate simulation traces, but how do we know
that the simulation results are correct? If we detect an error, is the error in
the model or in the simulation itself? This paper discusses the problem of
simulation safety, with the focus on hybrid modeling and simulation. In
particular, two key aspects are studied: safe zero-crossing detection and
deterministic hybrid event handling. The problems and solutions are discussed
and partially implemented in Modelica and Ptolemy II
ModelicaGym: Applying Reinforcement Learning to Modelica Models
This paper presents ModelicaGym toolbox that was developed to employ
Reinforcement Learning (RL) for solving optimization and control tasks in
Modelica models. The developed tool allows connecting models using Functional
Mock-up Interface (FMI) toOpenAI Gym toolkit in order to exploit Modelica
equation-based modelling and co-simulation together with RL algorithms as a
functionality of the tools correspondingly. Thus, ModelicaGym facilitates fast
and convenient development of RL algorithms and their comparison when solving
optimal control problem for Modelicadynamic models. Inheritance structure
ofModelicaGymtoolbox's classes and the implemented methods are discussed in
details. The toolbox functionality validation is performed on Cart-Pole
balancing problem. This includes physical system model description and its
integration using the toolbox, experiments on selection and influence of the
model parameters (i.e. force magnitude, Cart-pole mass ratio, reward ratio, and
simulation time step) on the learning process of Q-learning algorithm supported
with the discussion of the simulation results.Comment: accepted at EOOLT'1
A Rewriting-Logic-Based Technique for Modeling Thermal Systems
This paper presents a rewriting-logic-based modeling and analysis technique
for physical systems, with focus on thermal systems. The contributions of this
paper can be summarized as follows: (i) providing a framework for modeling and
executing physical systems, where both the physical components and their
physical interactions are treated as first-class citizens; (ii) showing how
heat transfer problems in thermal systems can be modeled in Real-Time Maude;
(iii) giving the implementation in Real-Time Maude of a basic numerical
technique for executing continuous behaviors in object-oriented hybrid systems;
and (iv) illustrating these techniques with a set of incremental case studies
using realistic physical parameters, with examples of simulation and model
checking analyses.Comment: In Proceedings RTRTS 2010, arXiv:1009.398
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