Control Algorithm Modeling Guidelines Using MATLAB®, Simulink®, and Stateflow®
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												  Armadillo C++ LibraryArmadillo: a template-based C++ library for linear algebra Conrad Sanderson and Ryan Curtin Abstract The C++ language is often used for implementing functionality that is performance and/or resource sensitive. While the standard C++ library provides many useful algorithms (such as sorting), in its current form it does not provide direct handling of linear algebra (matrix maths). Armadillo is an open source linear algebra library for the C++ language, aiming towards a good balance between speed and ease of use. Its high-level application programming interface (function syntax) is deliberately similar to the widely used Matlab and Octave languages [4], so that mathematical operations can be expressed in a familiar and natural manner. The library is useful for algorithm development directly in C++, or relatively quick conversion of research code into production environments. Armadillo provides efficient objects for vectors, matrices and cubes (third order tensors), as well as over 200 associated functions for manipulating data stored in the objects. Integer, floating point and complex numbers are supported, as well as dense and sparse storage formats. Various matrix factorisations are provided through integration with LAPACK [3], or one of its high performance drop-in replacements such as Intel MKL [6] or OpenBLAS [9]. It is also possible to use Armadillo in conjunction with NVBLAS to obtain GPU-accelerated matrix multiplication [7]. Armadillo is used as a base for other open source projects, such as MLPACK, a C++ library for machine learning and pattern recognition [2], and RcppArmadillo, a bridge between the R language and C++ in order to speed up computations [5].
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												  Scala TutorialScala Tutorial SCALA TUTORIAL Simply Easy Learning by tutorialspoint.com tutorialspoint.com i ABOUT THE TUTORIAL Scala Tutorial Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Scala has been created by Martin Odersky and he released the first version in 2003. Scala smoothly integrates features of object-oriented and functional languages. This tutorial gives a great understanding on Scala. Audience This tutorial has been prepared for the beginners to help them understand programming Language Scala in simple and easy steps. After completing this tutorial, you will find yourself at a moderate level of expertise in using Scala from where you can take yourself to next levels. Prerequisites Scala Programming is based on Java, so if you are aware of Java syntax, then it's pretty easy to learn Scala. Further if you do not have expertise in Java but you know any other programming language like C, C++ or Python, then it will also help in grasping Scala concepts very quickly. Copyright & Disclaimer Notice All the content and graphics on this tutorial are the property of tutorialspoint.com. Any content from tutorialspoint.com or this tutorial may not be redistributed or reproduced in any way, shape, or form without the written permission of tutorialspoint.com. Failure to do so is a violation of copyright laws. This tutorial may contain inaccuracies or errors and tutorialspoint provides no guarantee regarding the accuracy of the site or its contents including this tutorial. If you discover that the tutorialspoint.com site or this tutorial content contains some errors, please contact us at [email protected] TUTORIALS POINT Simply Easy Learning Table of Content Scala Tutorial ..........................................................................
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												  Cmsc 132: Object-Oriented Programming Ii© Department of Computer Science UMD CMSC 132: OBJECT-ORIENTED PROGRAMMING II Iterator, Marker, Observer Design Patterns Department of Computer Science University of Maryland, College Park © Department of Computer Science UMD Design Patterns • Descriptions of reusable solutions to common software design problems (e.g, Iterator pattern) • Captures the experience of experts • Goals • Solve common programming challenges • Improve reliability of solution • Aid rapid software development • Useful for real-world applications • Design patterns are like recipes – generic solutions to expected situations • Design patterns are language independent • Recognizing when and where to use design patterns requires familiarity & experience • Design pattern libraries serve as a glossary of idioms for understanding common, but complex solutions • Design patterns are used throughout the Java Class Libraries © Department of Computer Science UMD Iterator Pattern • Definition • Move through collection of objects without knowing its internal representation • Where to use & benefits • Use a standard interface to represent data objects • Uses standard iterator built in each standard collection, like List • Need to distinguish variations in the traversal of an aggregate • Example • Iterator for collection • Original • Examine elements of collection directly • Using pattern • Collection provides Iterator class for examining elements in collection © Department of Computer Science UMD Iterator Example public interface Iterator<V> { bool hasNext(); V next(); void remove();
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												  Iterators in C++ C 2016 All Rights ReservedSoftware Design Lecture Notes Prof. Stewart Weiss Iterators in C++ c 2016 All rights reserved. Iterators in C++ 1 Introduction When a program needs to visit all of the elements of a vector named myvec starting with the rst and ending with the last, you could use an iterative loop such as the following: for ( int i = 0; i < myvec.size(); i++ ) // the visit, i.e., do something with myvec[i] This code works and is a perfectly ne solution. This same type of iterative loop can visit each character in a C++ string. But what if you need code that visits all elements of a C++ list object? Is there a way to use an iterative loop to do this? To be clear, an iterative loop is one that has the form for var = start value; var compared to end value; update var; {do something with node at specic index} In other words, an iterative loop is a counting loop, as opposed to one that tests an arbitrary condition, like a while loop. The short answer to the question is that, in C++, without iterators, you cannot do this. Iterators make it possible to iterate through arbitrary containers. This set of notes answers the question, What is an iterator, and how do you use it? Iterators are a generalization of pointers in C++ and have similar semantics. They allow a program to navigate through dierent types of containers in a uniform manner. Just as pointers can be used to traverse a linked list or a binary tree, and subscripts can be used to traverse the elements of a vector, iterators can be used to sequence through the elements of any standard C++ container class.
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												  Generic ProgrammingGeneric Programming July 21, 1998 A Dagstuhl Seminar on the topic of Generic Programming was held April 27– May 1, 1998, with forty seven participants from ten countries. During the meeting there were thirty seven lectures, a panel session, and several problem sessions. The outcomes of the meeting include • A collection of abstracts of the lectures, made publicly available via this booklet and a web site at http://www-ca.informatik.uni-tuebingen.de/dagstuhl/gpdag.html. • Plans for a proceedings volume of papers submitted after the seminar that present (possibly extended) discussions of the topics covered in the lectures, problem sessions, and the panel session. • A list of generic programming projects and open problems, which will be maintained publicly on the World Wide Web at http://www-ca.informatik.uni-tuebingen.de/people/musser/gp/pop/index.html http://www.cs.rpi.edu/˜musser/gp/pop/index.html. 1 Contents 1 Motivation 3 2 Standards Panel 4 3 Lectures 4 3.1 Foundations and Methodology Comparisons ........ 4 Fundamentals of Generic Programming.................. 4 Jim Dehnert and Alex Stepanov Automatic Program Specialization by Partial Evaluation........ 4 Robert Gl¨uck Evaluating Generic Programming in Practice............... 6 Mehdi Jazayeri Polytypic Programming........................... 6 Johan Jeuring Recasting Algorithms As Objects: AnAlternativetoIterators . 7 Murali Sitaraman Using Genericity to Improve OO Designs................. 8 Karsten Weihe Inheritance, Genericity, and Class Hierarchies.............. 8 Wolf Zimmermann 3.2 Programming Methodology ................... 9 Hierarchical Iterators and Algorithms................... 9 Matt Austern Generic Programming in C++: Matrix Case Study........... 9 Krzysztof Czarnecki Generative Programming: Beyond Generic Programming........ 10 Ulrich Eisenecker Generic Programming Using Adaptive and Aspect-Oriented Programming .
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												  Generators & Coroutines Part 1: IteratorsGenerators & Coroutines Part 1: Iterators Edited Version of Slides by David Beazely http://www.dabeaz.comPart I Introduction to Iterators and Generators Monday, May 16, 2011 Monday, May 16, 2011 Copyright (C) 2008, http://www.dabeaz.com 1- 11 Iteration Iterating over a Dict As you know, Python has a "for" statement • • If you loop over a dictionary you get keys You use it to loop over a collection of items • >>> prices = { 'GOOG' : 490.10, >>> for x in [1,4,5,10]: ... 'AAPL' : 145.23, ... print x, ... 'YHOO' : 21.71 } ... ... 1 4 5 10 >>> for key in prices: >>> ... print key ... And, as you have probably noticed, you can YHOO • GOOG iterate over many different kinds of objects AAPL (not just lists) >>> Copyright (C) 2008, http://www.dabeaz.com 1- 12 Copyright (C) 2008, http://www.dabeaz.com 1- 13 Iterating over a String • If you loop over a string, you get characters >>> s = "Yow!" >>> for c in s: ... print c ... Y o w ! >>> Copyright (C) 2008, http://www.dabeaz.com 1- 14 Iterating over a Dict • If you loop over a dictionary you get keys >>> prices = { 'GOOG' : 490.10, ... 'AAPL' : 145.23, ... 'YHOO' : 21.71 } ... >>> for key in prices: ... print key ... YHOO GOOG AAPL >>> Copyright (C) 2008, http://www.dabeaz.com 1- 13 Iterating over a String Iterating over a File • If you loop over a file you get lines If you loop over a string, you get characters >>> for line in open("real.txt"): • ... print line, ... >>> s = "Yow!" Iterating over a File Real Programmers write in FORTRAN >>> for c in s: ..
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												  An Overview of the Scala Programming LanguageAn Overview of the Scala Programming Language Second Edition Martin Odersky, Philippe Altherr, Vincent Cremet, Iulian Dragos Gilles Dubochet, Burak Emir, Sean McDirmid, Stéphane Micheloud, Nikolay Mihaylov, Michel Schinz, Erik Stenman, Lex Spoon, Matthias Zenger École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne, Switzerland Technical Report LAMP-REPORT-2006-001 Abstract guage for component software needs to be scalable in the sense that the same concepts can describe small as well as Scala fuses object-oriented and functional programming in large parts. Therefore, we concentrate on mechanisms for a statically typed programming language. It is aimed at the abstraction, composition, and decomposition rather than construction of components and component systems. This adding a large set of primitives which might be useful for paper gives an overview of the Scala language for readers components at some level of scale, but not at other lev- who are familar with programming methods and program- els. Second, we postulate that scalable support for compo- ming language design. nents can be provided by a programming language which unies and generalizes object-oriented and functional pro- gramming. For statically typed languages, of which Scala 1 Introduction is an instance, these two paradigms were up to now largely separate. True component systems have been an elusive goal of the To validate our hypotheses, Scala needs to be applied software industry. Ideally, software should be assembled in the design of components and component systems. Only from libraries of pre-written components, just as hardware is serious application by a user community can tell whether the assembled from pre-fabricated chips.
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												  Cmsc 132: Object-Oriented Programming Ii© Department of Computer Science UMD CMSC 132: OBJECT-ORIENTED PROGRAMMING II Object-Oriented Programming Intro Department of Computer Science University of Maryland, College Park © Department of Computer Science UMD Object-Oriented Programming (OOP) • Approach to improving software • View software as a collection of objects (entities) • OOP takes advantage of two techniques • Abstraction • Encapsulation © Department of Computer Science UMD Techniques – Abstraction • Abstraction • Provide high-level model of activity or data • Don’t worry about the details. What does it do, not how • Example from outside of CS: Microwave Oven • Procedural abstraction • Specify what actions should be performed • Hide algorithms • Example: Sort numbers in an array (is it Bubble sort? Quicksort? etc.) • Data abstraction • Specify data objects for problem • Hide representation • Example: List of names • Abstract Data Type (ADT) • Implementation independent of interfaces • Example: The ADT is a map (also called a dictionary). We know it should associate a key with a value. Can be implemented in different ways: binary search tree, hash table, or a list. © Department of Computer Science UMD Techniques – Encapsulation • Encapsulation • Definition: A design technique that calls for hiding implementation details while providing an interface (methods) for data access • Example: use the keyword private when designing Java classes • Allow us to use code without having to know its implementation (supports the concept of abstraction) • Simplifies the process of code
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												  Automated Annotation of Simulink Generated C Code Based on the Simulink ModelDEGREE 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.
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												  Lecture #4: Simulation of Hybrid SystemsEmbedded 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
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												  Introduction to SimulinkIntroduction 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
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												  FMI Target for Simulink Coder, It Is Now Possible to Export Models from Simulink to Any Platform That Supports Fmus for Co-SimulationSupporting 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.