EVALUATION of a MODELING and AUTOMATIC C CODE GENERATION TOOLSET AS an OPEN SOURCE ALTERNATIVE SOLUTION William Fotso Kom, Xavier Querol
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Basic Scicoslab and Scicos
Florence University, October 2010 Basic ScicosLab and Scicos ScicosLab/Scicos for dummies like us Simone Mannori – ScicosLab/Scicos developer 1 Florence University, October 2010 Basic ScicosLab and Scicos Digital control systems design and simulation: the Bermuda Triangle for engineers Computer science Control systems Physics Simone Mannori – ScicosLab/Scicos developer 2 Florence University, October 2010 Basic ScicosLab and Scicos What ScicosLab is? An interpreted language (“Scilab Language”, very similar _but_not_equal_ to Matlab) Full support for matrix computation (BLAS, LAPACK, single and multi cores support using ACML now, GPU support in the near future using OpenCL). Basic programming (just like Matlab) GUI development (using TCL/TK and UICONTROL) Linear algebra Polynomial calculation Control systems modelling and design (continuous, discrete and hybrid systems) Robust control toolbox Optimization and simulation (build-in solvers) Signal processing (filters design, etc.) ARMA modelling and simulation Basic statistics Basic identification PVM support TCL/TK and Java support Max-plus algebra toolbox Simone Mannori – ScicosLab/Scicos developer 3 Florence University, October 2010 Basic ScicosLab and Scicos What you can do with ScicosLab? • Interact with the command line • Write a program, one line at time, using the internal or with an external editor • Run the program in an user friendly interpreted environment (easy debug) • Use the rich embedded library. • You can develop your ScicosLab functions using C, Fortran, Java, etc. • Produce nice graphics diagrams. Simone Mannori – ScicosLab/Scicos developer 4 Florence University, October 2010 Basic ScicosLab and Scicos What Scicos is? Scicos is a graphical dynamical system modeller and simulator developed inside the METALAU project at INRIA, Paris Rocquencourt centre. -
RTAI-Lab Tutorial: Scicoslab, Comedi, and Real-Time Control
RTAI-Lab tutorial: Scicoslab, Comedi, and real-time control Roberto Bucher 1 Simone Mannori Thomas Netter 2 May 24, 2010 Summary RTAI-Lab is a tool chain for real-time software and control system development. This tutorial shows how to install the various components: the RTAI real-time Linux kernel, the Comedi interface for control and measurement hardware, the Scicoslab GUI-based CACSD modeling software and associated RTAI-Lab blocks, and the xrtailab interactive oscilloscope. RTAI-Lab’s Scicos blocks are detailed and examples show how to develop elementary block diagrams, automatically generate real-time executables, and add custom elements. 1Main RTAI-Lab developer, person to contact for technical questions: roberto.bucher at supsi.ch, see page 46 Contents 1 Introduction 4 1.1 RTAI-Lab tool chain . .4 1.2 Commercial software . .4 2 Installation 5 2.1 Requirements . .5 2.1.1 Hardware requirements . .5 2.1.2 Software requirements . .6 2.2 Mesa library . .7 2.3 EFLTK library . .7 2.4 Linux kernel and RTAI patch . .7 2.5 Comedilib . .8 2.6 RTAI (1st pass) . .8 2.7 RTAI tests . .9 2.8 Comedi . .9 2.9 RTAI (2nd pass) . 10 2.10 ScicosLab . 11 2.11 RTAI-Lab add-ons to Scicoslab-4.4 . 11 2.12 User configuration for scicoslab-4.4 . 11 2.13 Load the modules . 11 3 Development with RTAI-Lab 13 3.1 Boot Linux-RTAI . 13 3.2 Start Scicos . 13 3.3 RTAI-Lib palette . 14 3.4 Real-time sinewave: step by step . 16 3.4.1 Create block diagram . -
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. -
Máster Universitario En Investigación En Inteligencia Artificial
Identificador : 4311243 IMPRESO SOLICITUD PARA MODIFICACIÓN DE TÍTULOS OFICIALES 1. DATOS DE LA UNIVERSIDAD, CENTRO Y TÍTULO QUE PRESENTA LA SOLICITUD De conformidad con el Real Decreto 1393/2007, por el que se establece la ordenación de las Enseñanzas Universitarias Oficiales UNIVERSIDAD SOLICITANTE CENTRO CÓDIGO CENTRO Universidad Nacional de Educación a Distancia Escuela Técnica Superior de Ingeniería 28050756 Informática NIVEL DENOMINACIÓN CORTA Máster Investigación en Inteligencia Artificial DENOMINACIÓN ESPECÍFICA Máster Universitario en Investigación en Inteligencia Artificial por la Universidad Nacional de Educación a Distancia RAMA DE CONOCIMIENTO CONJUNTO Ingeniería y Arquitectura No HABILITA PARA EL EJERCICIO DE PROFESIONES NORMA HABILITACIÓN REGULADAS No SOLICITANTE NOMBRE Y APELLIDOS CARGO EMILIO LETÓN MOLINA Coordinador del Máster en Investigación en Inteligencia Artificial Tipo Documento Número Documento NIF REPRESENTANTE LEGAL NOMBRE Y APELLIDOS CARGO ALEJANDRO TIANA FERRER Rector Tipo Documento Número Documento NIF RESPONSABLE DEL TÍTULO NOMBRE Y APELLIDOS CARGO RAFAEL MARTINEZ TOMAS Director de la Escuela Técnica Superior de Ingeniería Informática de la Universidad Nacional de Educación a Distancia Tipo Documento Número Documento NIF 2. DIRECCIÓN A EFECTOS DE NOTIFICACIÓN A los efectos de la práctica de la NOTIFICACIÓN de todos los procedimientos relativos a la presente solicitud, las comunicaciones se dirigirán a la dirección que figure en el presente apartado. DOMICILIO CÓDIGO POSTAL MUNICIPIO TELÉFONO Bravo Murillo, 38 28015 Madrid E-MAIL PROVINCIA FAX Madrid 1 / 66 Verificable en https://sede.educacion.gob.es/cid y Carpeta Ciudadana (https://sede.administracion.gob.es) CSV: 298753191227704287823872 Identificador : 4311243 3. PROTECCIÓN DE DATOS PERSONALES De acuerdo con lo previsto en la Ley Orgánica 5/1999 de 13 de diciembre, de Protección de Datos de Carácter Personal, se informa que los datos solicitados en este impreso son necesarios para la tramitación de la solicitud y podrán ser objeto de tratamiento automatizado. -
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 -
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. -
A Comparison of Software Engines for Simulation of Closed-Loop Control Systems
New Jersey Institute of Technology Digital Commons @ NJIT Theses Electronic Theses and Dissertations Spring 5-31-2010 A comparison of software engines for simulation of closed-loop control systems Sanket D. Nikam New Jersey Institute of Technology Follow this and additional works at: https://digitalcommons.njit.edu/theses Part of the Electrical and Electronics Commons Recommended Citation Nikam, Sanket D., "A comparison of software engines for simulation of closed-loop control systems" (2010). Theses. 68. https://digitalcommons.njit.edu/theses/68 This Thesis is brought to you for free and open access by the Electronic Theses and Dissertations at Digital Commons @ NJIT. It has been accepted for inclusion in Theses by an authorized administrator of Digital Commons @ NJIT. For more information, please contact [email protected]. Cprht Wrnn & trtn h prht l f th Untd Stt (tl , Untd Stt Cd vrn th n f phtp r thr rprdtn f prhtd trl. Undr rtn ndtn pfd n th l, lbrr nd rhv r thrzd t frnh phtp r thr rprdtn. On f th pfd ndtn tht th phtp r rprdtn nt t b “d fr n prp thr thn prvt td, hlrhp, r rrh. If , r rt fr, r ltr , phtp r rprdtn fr prp n x f “fr tht r b lbl fr prht nfrnnt, h ntttn rrv th rht t rf t pt pn rdr f, n t jdnt, flfllnt f th rdr ld nvlv vltn f prht l. l t: h thr rtn th prht hl th r Inttt f hnl rrv th rht t dtrbt th th r drttn rntn nt: If d nt h t prnt th p, thn lt “ fr: frt p t: lt p n th prnt dl rn h n tn lbrr h rvd f th prnl nfrtn nd ll ntr fr th pprvl p nd brphl th f th nd drttn n rdr t prtt th dntt f I rdt nd flt. -
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 Novel Code Generation Methodology for Block Diagram Modeler And
A novel code generation methodology for block diagram modeler and simulators Scicos and VSS Jean-Philippe Chancelier∗ Ramine Nikoukhah∗ Universit Paris-Est, Cermics (ENPC) Altair Engineering October 25, 2018 Abstract Block operations during simulation in Scicos and VSS environments can naturally be de- scribed as Nsp functions. But the direct use of Nsp functions for simulation leads to poor performance since the Nsp language is interpreted, not compiled. The methodology presented in this paper is used to develop a tool for generating efficient compilable code, such as C and ADA, for Scicos and VSS models from these block Nsp functions. Operator overloading and partial evaluation are the key elements of this novel approach. This methodology may be used in other simulation environments such as Matlab/Simulink. Contents 1 Introduction 2 2 Simple Example 4 3 Operator overloading and pseudo-code generation 9 3.1 Newdatatypesandoverloading . ....... 10 4 Code generation directives 13 4.1 Creating persistent variables . ........... 13 4.2 Creatingfunctionarguments . ........ 14 arXiv:1510.02789v1 [cs.MS] 8 Oct 2015 4.3 Functionconstant ................................ ..... 14 4.4 Functionexpand .................................. 15 4.5 Functions bvarcopy and bvarempty . ......... 15 5 Application to code generation for Scicos 16 5.1 Codegenerationscript . ....... 16 5.2 Conditionalsubsampling. ........ 17 5.3 Example......................................... 21 6 Conclusion 23 ∗ This code generation tool was developed during a three-years research projects funded within the French FUI 2011 called “Projet P” [6] 1 A C code generated for the extended Kalman filter example 23 1 Introduction In this paper we present a new methodology of code generation for block diagram simulation tools such as Scicos [2], VisSim SIMULATE1 (VSS) and Simulink. -
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.