Integrating AADL and FMI to Extend Virtual Integration Capability

Virtual Integration Capability is paramount to perform early validation of Cyber Physical Systems. The objective is to guide the systems engineer so as to ensure that the system under design meets multiple criteria through high-fidelity simulation. In this paper, we present an integration scheme that leverages the FMI (Functional Mock-Up interface) standard and the AADL architecture description language. Their combination allows for validation of systems combining embedded platform captured by the AADL, and FMI components that represent physical elements, either mechanical parts, or the environment. We present one approach, and demonstrator case studies

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

Multi-domain Modeling and Simulation

One starting point for the analysis and design of a control system is the block diagram representation of a plant. Since it is nontrivial to convert a physical model of a plant into a blockk diagram, this can be performed manually only for small models. Based on reseach from the last 40 years, more andmore mature tools are available to achieve this transformation fully automatically. As a result, multi-domain plants, for example, systems with electrical, mechanical, thermal, and fluid parts, can be modled in a unified way and can be used directly as input-output blocks for control system design. An overview of the basic principles of this approach is given, and it is shown how to utilize nonlinear, multidomain plant models directly in a controller. Finally, the low-level "Functional Mockup Interface" standard is sketched to exchang multi-domain models between many different modeling and simulation environments

Framework for Simulation of Coupled Systems by Aggregation

Complex systems can be described by coupling several standalone ODE problems that communicate with input and output signals. The need for simulating such systems has increased in recent years. A major issue when simulating coupled ODE systems has been to communicate coupling relations properly throughout integration and how to handle discontinuities. In this paper a concept that aggregates several ODEs into a single problem is presented. For each right-hand-side function evaluation the aggregated problem communicates coupling relations ensuring that all inputs and outputs in the system are uptodate. Experiments are conducted on systems containing algebraic loops, discontinuities and non-linear couplings; the results suggest potential for the concept

NeuralFMU: presenting a workflow for integrating hybrid neuralODEs into real-world applications

The term NeuralODE describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU

Integrated Platform for Whole Building HVAC System Automation and Simulation

Integrated optimal control strategies can reduce the overall building HVAC system energy consumption as well as improved air quality resulting in improved health and cognitive function for the occupants. However, it is time consuming to quantitatively evaluate the design-intended building HVAC automation system performance before on-site deployment, because: 1) the building and HVAC system design specs are in 2D or 3D drawings that require significant efforts to develop the system steady state or dynamic models based on them; 2) the building HVAC control strategies are designed and implemented in building automation (BA) system that could not smoothly connect with the building HVAC system steady state or dynamic models for performance evaluation through close-loop simulation. This paper presents the tool chain of an integrated simulation platform for building HVAC system automation and simulation as well as its implementation in a real case. First, building information from a Revit BIM model is automatically parsed to an EnergyPlus building energy model. Second, the HVAC system model is quickly populated with a scalable HVAC system library in Dymola. Third, the HVAC controls are developed in WebCTRL, a building HVAC automation system by Automated Logic Corporation (ALC). Finally, both the building energy model and HVAC system model are wrapped up as Functional Mock-up Units (FMU) and connected with embedded simulator in WebCTRL to perform close-loop building automation system performance simulation. A real case study, a chiller plant system in a hotel building, is conducted to verify the scalability and benefit of the developed tool chain. The case study demonstrates the values in identifying both HVAC automation system design-intended control issues and improvement areas for integrated optimal controls. This platform enables testing of building HVAC control strategies before on-site deployment, which reduces the labor and time required for building HVAC control development-to-market process and ensure the delivering quality. Furthermore, this platform can be calibrated with metered real-time data from the specific building HVAC system and serve as its “digital twin” that empowers the system fault detection, diagnostics and predictive maintenance

FMI for Co-Simulation of Embedded Control Software

Increased complexity of cyber-physical systems within the maritime industry demands closer cooperation be-tween engineering disciplines. The functional mockup interface (FMI) is an initiative aiding cross-discipline in-teraction by providing, a widely accepted, standard for model exchange and co-simulation. The standard is sup-ported by a number of modelling tools. However, to im-plement it on an existing platform requires adaptation. This paper investigates how to adapt the software of an embedded control system to comply with the FMI for co-simulation standard. In particular, we suggest a way of advancing the clock of a real time operating system (RTOS), by overwriting the idle thread and waiting for a signal to start execution until return to idle. This ap-proach ensures a deterministic and temporal execution of the simulation across multiple nodes. As proof of concept, a co-simulation is conducted, showing that the control system of an SCR (selective catalyst reduction) emission reduction system can be packed in a functional mockup unit (FMU) and co-simulated with a physical model, built in Ptolemy II. Results show that FMI can be used for co-simulation of an embedded SCR control soft-ware and for control software development

Systems Engineering for Cyber-Physical Products

International audienceThis paper will present how the Dassault Systèmes PLM solution introduces a new paradigm to address the systems engineering challenges of developing cyber-physical systems. V6 unified modeling architecture has extensive support for cross discipline systems engineering based tools, enabling a collaborative Platform and Model Based Engineering environment