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PCLTA-21 PCI Network Adapter Models 74501, 74502, 74503, and 74504
PCLTA-21 PCI Network Adapter Models 74501, 74502, 74503, and 74504 ▼ Universal 32-bit PCI adapter card for LONWORKS® networks for PCs with 3.3V or 5V PCI ▼ Plug-and-play network driver compatible with MicrosoftWindows® 98/2000 and Windows XP ▼ Downloadable firmware allows updates without accessing or changing hardware ▼ Integral FT 3150® Free Topology Smart Transceiver, RS-485, TPT/XF-78, or TPT/XF-1250 transceiver ▼ LNS® Network Services Interface (NSI) supports LNS applications ▼ Layer 5 MIP for use with OpenLDV™ driver Description ▼ CE Mark, U.L. Listed, cU.L. Listed The PCLTA-21 Network Adapter is a high-perform- ance LONWORKS interface for personal computers equipped with a 3.3V or 5V 32-bit Peripheral The NSI mode of the PCLTA-21 adapter is used Component Interconnect (PCI) interface and a compati- with applications based on the LNS network operating system such as the LonMaker Tool, or the LNS DDE ble operating system. Designed for use in LONWORKS control networks that require a PC for monitoring, Server. managing, or diagnosing the network, the PCLTA-21 The MIP mode of the PCLTA-21 adapter is used with adapter is ideal for industrial control, building automa- applications based on OpenLDV. tion, and process control applications. The PCLTA-21 In both NSI and MIP modes, the adapter permits the adapter features an integral twisted pair transceiver, host PC to act as a LONWORKS application device, such downloadable memory, a network management inter- that the PC is running application-specific programs face, and plug-and-play capability with Microsoft while the adapter handles lower layer functions such as Windows 98/2000 and Windows XP. -
Automated Annotation of Simulink Generated C Code Based on the Simulink Model
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Automated Annotation of Simulink Generated C Code Based on the Simulink Model SREEYA BASU ROY KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Automated Annotation of Simulink Generated C Code Based on the Simulink Model SREEYA BASU ROY Master in Embedded Systems Date: September 25, 2020 Supervisor: Predrag Filipovikj Examiner: Matthias Becker School of Electrical Engineering and Computer Science Host company: Scania CV AB Swedish title: Automatisk Kommentar av Simulink Genererad C kod Baserad på Simulink Modellen iii Abstract There has been a wave of transformation in the automotive industry in recent years, with most vehicular functions being controlled electron- ically instead of mechanically. This has led to an exponential increase in the complexity of software functions in vehicles, making it essential for manufactures to guarantee their correctness. Traditional software testing is reaching its limits, consequently pushing the automotive in- dustry to explore other forms of quality assurance. One such technique that has been gaining momentum over the years is a set of verification techniques based on mathematical logic called formal verification tech- niques. Although formal techniques have not yet been adopted on a large scale, these methods offer systematic and possibly more exhaus- tive verification of the software under test, since their fundamentals are based on the principles of mathematics. In order to be able to apply formal verification, the system under test must be transformed into a formal model, and a set of proper- ties over such models, which can then be verified using some of the well-established formal verification techniques, such as model check- ing or deductive verification. -
Systematic Testing of the Continuous Behavior of Automotive Systems
Systematic Testing of the Continuous Behavior of Automotive Systems Eckard Bringmann Andreas Krämer DaimlerChrysler DaimlerChrysler Alt-Moabit 96a Alt-Moabit 96a 10559 Berlin, Germany 10559 Berlin, Germany +49 30 39982 242 +49 30 39982 336 [email protected] [email protected] ABSTRACT gap, the testing of automotive systems in practice focuses on In this paper, we introduce a new test method that enables the simple data tables to describe input signals or on script languages, systematic definition of executable test cases for testing the such as Visual Basic, Python or Perl, to automate tests. continuous behavior of automotive embedded systems. This Nevertheless, signals are still very difficult to handle in these method is based on a graphical notation for test cases that is not languages. Even worse, there is no systematic procedure to help only easy to understand but also powerful enough to express very testers reveal redundancies and missing, test relevant, aspects complex, fully automated tests as well as reactive tests. This new within their test cases. In other words, the selection of test data approach is already in use in several production-vehicle occurs ad hoc and is based on some use cases and typical extreme development projects at DaimlerChrysler and at some suppliers. scenarios but often does not cover all functional requirements of the system under test (SUT). Categories and Subject Descriptors Our new approach – which is called Time Partition Testing D2.5 [Software Engineering]: Testing -
Lecture #4: Simulation of Hybrid Systems
Embedded Control Systems Lecture 4 – Spring 2018 Knut Åkesson Modelling of Physcial Systems Model knowledge is stored in books and human minds which computers cannot access “The change of motion is proportional to the motive force impressed “ – Newton Newtons second law of motion: F=m*a Slide from: Open Source Modelica Consortium, Copyright © Equation Based Modelling • Equations were used in the third millennium B.C. • Equality sign was introduced by Robert Recorde in 1557 Newton still wrote text (Principia, vol. 1, 1686) “The change of motion is proportional to the motive force impressed ” Programming languages usually do not allow equations! Slide from: Open Source Modelica Consortium, Copyright © Languages for Equation-based Modelling of Physcial Systems Two widely used tools/languages based on the same ideas Modelica + Open standard + Supported by many different vendors, including open source implementations + Many existing libraries + A plant model in Modelica can be imported into Simulink - Matlab is often used for the control design History: The Modelica design effort was initiated in September 1996 by Hilding Elmqvist from Lund, Sweden. Simscape + Easy integration in the Mathworks tool chain (Simulink/Stateflow/Simscape) - Closed implementation What is Modelica A language for modeling of complex cyber-physical systems • Robotics • Automotive • Aircrafts • Satellites • Power plants • Systems biology Slide from: Open Source Modelica Consortium, Copyright © What is Modelica A language for modeling of complex cyber-physical systems -
Introduction to Simulink
Introduction to Simulink Mariusz Janiak p. 331 C-3, 71 320 26 44 c 2015 Mariusz Janiak All Rights Reserved Contents 1 Introduction 2 Essentials 3 Continuous systems 4 Hardware-in-the-Loop (HIL) Simulation Introduction Simulink is a block diagram environment for multidomain simulation and Model-Based Design. It supports simulation, automatic code generation, and continuous test and verification of embedded systems.1 Graphical editor Customizable block libraries Solvers for modeling and simulating dynamic systems Integrated with Matlab Web page www.mathworks.com 1 The MathWorks, Inc. Introduction Simulink capabilities Building the model (hierarchical subsystems) Simulating the model Analyzing simulation results Managing projects Connecting to hardware Introduction Alternatives to Simulink Xcos (www.scilab.org/en/scilab/features/xcos) OpenModelica (www.openmodelica.org) MapleSim (www.maplesoft.com/products/maplesim) Wolfram SystemModeler (www.wolfram.com/system-modeler) Introduction Model based design with Simulink Modeling and simulation Multidomain dynamic systems Nonlinear systems Continuous-, Discrete-time, Multi-rate systems Plant and controller design Rapidly model what-if scenarios Communicate design ideas Select/Optimize control architecture and parameters Implementation Automatic code generation Rapid prototyping for HIL, SIL Verification and validation Essentials Working with Simulink Launching Simulink Library Browser Finding Blocks Getting Help Context sensitive help Simulink documentation Demo Working with a simple model Changing -
Automated Unit Testing in Model-Based Embedded Software Development
Automated Unit Testing in Model-based Embedded Software Development Christoph Luckeneder1, Hermann Kaindl1 and Martin Korinek2 1Institute of Computer Technology, TU Wien, Vienna, Austria 2Robert Bosch AG, Gollnergasse¨ 15-17, Vienna, Austria Keywords: Automated Testing, Unit Tests, Model-based Development, Embedded Software, Safety-critical Systems, Automotive. Abstract: Automating software tests is generally desirable, and especially for the software of safety-critical real-time systems such as automotive control systems. For such systems, also conforming with the ISO 26262 standard for functional safety of road vehicles is absolutely necessary. These are embedded systems, however, which pose additional challenges with regard to test automation. In particular, the questions arise on which hardware platform the tests should be performed and by use of which workflow and tools. This is especially relevant in terms of cost, while still ensuring conformance with ISO 26262. In this paper, we present a practical approach for automated unit testing in model-based embedded software development for a safety-critical automotive application. Our approach includes both a workflow and sup- porting tools for performing automated unit tests. In particular, we analyze an as-is workflow and propose changes to the workflow for reducing costs and time needed for performing such tests. In addition, we present an improved tool chain for supporting the test workflow. In effect, without manually implementing each test case twice unit tests can be performed both in a simulation environment and on an open-loop test environment including the embedded platform target hardware. 1 INTRODUCTION The following support through a tool chain was planned: modeling and simulation of the resulting Automotive systems have more and more become software models, as well as model-based test automa- software-intensive systems, which include large-scale tion. -
FMI Target for Simulink Coder, It Is Now Possible to Export Models from Simulink to Any Platform That Supports Fmus for Co-Simulation
Supporting your vision Cross-Platform Modeling with FMI Target for Simulink® Coder™ Open technology standards for an integrated product lifecycle Engine Transmission Thermal EV/HV Chassis Compo- Models Models Systems Models nents Models Functional Mock-Up Interface The challenge for manufacturers of complex machinery that heavily rely on components from suppliers is the seamless exchange of data and specifications during the development. The same holds true for large corporates with multiple R&D departments at various locations using several development tools due to different objectives. Those challenges include different programming languages from the various tools, the lack of standardized model interfaces and the concerns for the protection of intellectual property. The development of the Functional Mock-up Interface (FMI) has enabled software-, model- and hardware-in-the-loop simulations with dynamic system models from different software environments. With the FMI Target for Simulink Coder, it is now possible to export models from Simulink to any platform that supports FMUs for Co-Simulation. FMI for Co-Simulation The goal is to couple two or more models with solvers in a co-simulation environment. The data exchange between subsystems is restricted to discrete communication points. The subsystems are processed independently from each other by their individual solvers during the time interval between two communication points. Master algorithms control the synchronization of all slave simulation solvers and the data exchange between the subsystems. The interface allows for both standard and advanced master algorithms, such as variable communication step sizes, signal extrapolation of higher order and error checking. FMI Target for Simulink® Coder™ FMI for Co-Simulation For the exchange of models across different platforms, the FMI Target for Simulink Coder enables the export of models from Simulink as FMUs for Co-Simulation. -
Making Simulink Models Robust with Respect to Change Making Simulink Models Robust with Respect to Change
Making Simulink Models Robust with Respect to Change Making Simulink Models Robust with Respect to Change By Monika Jaskolka, B.Co.Sc., M.A.Sc. a thesis submitted to the Department of Computing and Software and the School of Graduate Studies of McMaster University in partial fulfillment of the requirements for the degree of Doctor of Philosophy Doctor of Philosophy (December 2020) McMaster University (Software Engineering) Hamilton, ON, Canada Title: Making Simulink Models Robust with Respect to Change Author: Monika Jaskolka B.Co.Sc. (Laurentian University) M.A.Sc. (McMaster University) Supervisors: Dr. Mark Lawford, Dr. Alan Wassyng Number of Pages: xi, 206 ii For my husband, Jason iii Abstract Model-Based Development (MBD) is an approach that uses software models to describe the behaviour of embedded software and cyber-physical systems. MBD has become an increasingly prevalent paradigm, with Simulink by MathWorks being the most widely used MBD platform for control software. Unlike textual programming languages, visual languages for MBD such as Simulink use block diagrams as their syntax. Thus, some software engineering principles created for textual languages are not easily adapted to this graphical notation or have not yet been supported. A software engineering principle that is not readily supported in Simulink is the modularization of systems using information hiding. As with all software artifacts, Simulink models must be constantly maintained and are subject to evolution over their lifetime. This principle hides likely changes, thus enabling the design to be robust with respect to future changes. In this thesis, we perform repository mining on an industry change management system of Simulink models to understand how they are likely to change. -
Modeling and Simulation in Scilab/Scicos Mathematics Subject Classification (2000): 01-01, 04-01, 11 Axx, 26-01
Stephen L. Campbell, Jean-Philippe Chancelier and Ramine Nikoukhah Modeling and Simulation in Scilab/Scicos Mathematics Subject Classification (2000): 01-01, 04-01, 11 Axx, 26-01 Library of Congress Control Number: 2005930797 ISBN-10: 0-387-27802-8 ISBN-13: 978-0387278025 Printed on acid-free paper. © 2006 Springer Science + Business Media, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, Inc., 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaption, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. (BPR/HAM) 9 8 7 6 5 4 3 2 1 springeronline.com Preface Scilab (http://www.scilab.org) is a free open-source software package for scientific com- putation. It includes hundreds of general purpose and specialized functions for numerical computation, organized in libraries called toolboxes that cover such areas as simulation, optimization, systems and control, and signal processing. These functions reduce consid- erably the burden of programming for scientific applications. One important Scilab toolbox is Scicos. Scicos (http://www.scicos.org) provides a block-diagram graphical editor for the construction and simulation of dynamical sys- tems. -
A Modelica Power System Library for Phasor Time-Domain Simulation
2013 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), October 6-9, Copenhagen 1 A Modelica Power System Library for Phasor Time-Domain Simulation T. Bogodorova, Student Member, IEEE, M. Sabate, G. Leon,´ L. Vanfretti, Member, IEEE, M. Halat, J. B. Heyberger, and P. Panciatici, Member, IEEE Abstract— Power system phasor time-domain simulation is the FMI Toolbox; while for Mathematica, SystemModeler often carried out through domain specific tools such as Eurostag, links Modelica models directly with the computation kernel. PSS/E, and others. While these tools are efficient, their individual The aims of this article is to demonstrate the value of sub-component models and solvers cannot be accessed by the users for modification. One of the main goals of the FP7 iTesla Modelica as a convenient language for modeling of complex project [1] is to perform model validation, for which, a modelling physical systems containing electric power subcomponents, and simulation environment that provides model transparency to show results of software-to-software validations in order and extensibility is necessary. 1 To this end, a power system to demonstrate the capability of Modelica for supporting library has been built using the Modelica language. This article phasor time-domain power system models, and to illustrate describes the Power Systems library, and the software-to-software validation carried out for the implemented component as well as how power system Modelica models can be shared across the validation of small-scale power system models constructed different simulation platforms. In addition, several aspects of using different library components. Simulations from the Mo- using Modelica for simulation of complex power systems are delica models are compared with their Eurostag equivalents. -
OMG Sysphs: Integrating Sysml, Simulink, Modelica And
OMG SysPhs: Integrating SysML, Simulink, Modelica and FMI | ref.: 3DS_Document_2015 | ref.: 2/13/20 | Confidential Information | Information | Confidential Nerijus Jankevicius Systèmes CATIA | No Magic Dassault © INCOSE IW, Torrance, Jan 27, 2020 3DS.COM System Model as an Integration Framework © 2012-2014 by Sanford Friedenthal SysML as co-simulation environment 35 Reduce and standardize mappings 4 Unified Physics Domain Flowing Substance Flow rate Potential to flow Electrical Charge Current Voltage Hydraulic Volume Volumetric flow rate Pressure Rotational Angular momentum Torque Angular velocity Translational Linear momentum Force Velocity Thermal Entropy Entropy flow Temperature flow rate = amount of substance/time flow rate * potential = energy / time = power The Standard : SysPhs •SysPhS - https://www.omg.org/spec/SysPhS/1.0 • SysML Mapping to SiMulink and Modelica • SysPhS profile • SysPhS library Simulation profile Modelica vs Simulink • Modelica • SiMulink • Language is better suited for physical • Language is well-suited for control modeling (plant) algorithMs • Object oriented approach for • TransforMational seMantics of signals modeling physical and signal processing coMponents (Mechanical, electrical, etc.) • Causal seMantics (inputs -> outputs) • Causal and A-Causal seMantics • Well integrated into the “MATLAB (equations) universe” • Open standard (of the textual • Widely used in industry (standard de- language) facto) • Multi tool support (although DyMola • Many existing tool integrations is doMinant) • Code generation to -
Testwell CTC++ Test Coverage Analyser
Testwell CTC++ Test Coverage Analyser What is Testwell CTC++? Testwell CTC++ is the leading Code Coverage Tool for measuring Code Coverage on host and on all embedded targets (even very small ones). The tool can be used to fulfill the code coverage requirements of safety standards like DO-178C, ISO 26262, EN 50128, and IEC 60880. Hundreds of companies in more than 30 countries all over the world use Testwell CTC++ in order to insure the quality of their softwares. Testwell CTC++ is the first choice for companies which have to achieve and to proof high code coverage in aerospace, automotive, transportation, healthcare, nuclear power and other industries. Why Code Coverage? Code coverage is a measure, which describes the degree to which the source code of a program is tested. This can be considered as an indirect measure of quality of the software. Code coverage finds areas of a program which have not yet exercised by a set of test cases. This way it supports you by creating additional test cases to increase the code coverage and prevents you from writing redundant test cases. Code coverage is most useful during the module testing phase, though it also has benefit during integration testing and at other times, depending on how and what you are testing. Code coverage is « highly recommanded » (which means de facto mandatory) for safety critical software. Safety standards like DO-178C (Software Considerations in Airborne Systems and Equipment Certification), IEC/EN 61508 (functional safety of electrical/electronic, programmable electronic safety-related systems), EN 50128 (Railway applications - Communication, signalling and processing systems - Software for railway control and protection systems), IEC 60880 (nuclear power), and ISO 26262 (functional safety of road vehicles) request different levels of code coverage according to the safety level of the application.