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Engineering and Scientific Computing with Scilab

Engineering and Scientific Computing with Scilab

Overview

Overview Scilab is a scientific software package that provides a powerful open com- puting environment for engineering and scientific applications. Distributed freely via the Internet since 1994, Scilab is currently being used in educa- tional and industrial environments around the world. This book contains all the information needed to master Scilab: how to use it interactively as a super calculator, how to write programs, how to de- velop complex applications, and more. The authors, Carey Bunks (BBN1), Jean-Philippe Chancelier (ENPC2), Fran ois Delebecque, Claude Gomez, 3 Maurice Goursat, Ramine Nikoukhah, and Serge Steer (INRlA ), have not only been involved in the development of Scilab, but have used it for teach- ing and industrial applications for many years. A CD-ROM, containing the entire Scilab soUrce code as well as a set of precompiled binary executables for a variety of computing platforms, is included with this book. The objective here is to give a thorough description of Scilab's use, in- cluding how to master its environment and programming language, the use of the integrated graphics, the incorporation of user-provided func- tions, and a tour of the numerous application toolboxes. The purpose is to provide students and professionals with an introduction to Scilab and its use in engineering and scientific problem solving. The numerous practical examples serve as a framework that can be used as a basis for developing other applications.

Product Details

ISBN-13: 9781461272045
Publisher: Birkhäuser Boston
Publication date: 11/01/2012
Edition description: Softcover reprint of the original 1st ed. 1999
Pages: 491
Product dimensions: 7.01(w) x 10.00(h) x 0.04(d)

Table of Contents

I The Scilab Package.- 1 Introduction.- 1.1 What Is Scilab?.- 1.2 Getting Started.- 2 The Scilab Language.- 2.1 Constants.- 2.1.1 Real Numbers.- 2.1.2 Complex Numbers.- 2.1.3 Character Strings.- 2.1.4 Special Constants.- 2.2 Data Types.- 2.2.1 Matrices of Numbers.- 2.2.2 Sparse Matrices of Numbers.- 2.2.3 Matrices of Polynomials.- 2.2.4 Boolean Matrices.- 2.2.5 Sparse Boolean Matrices.- 2.2.6 String Matrices.- 2.2.7 Lists.- 2.2.8 Typed Lists.- 2.2.9 Functions of Rational Matrices.- 2.2.10 Functions and Libraries.- 2.3 Scilab Syntax.- 2.3.1 Variables.- 2.3.2 Assignments.- 2.3.3 Expressions.- 2.3.4 The list and tlist Operations.- 2.3.5 Flow Control.- 2.3.6 Functions and Scripts.- 2.3.7 Commands.- 2.4 Data-Type-Related Functions.- 2.4.1 Type Conversion Functions.- 2.4.2 Type Enquiry Functions.- 2.5 Overloading.- 2.5.1 Operator Overloading.- 2.5.2 Primitive Functions.- 2.5.3 How to Customize the Display of Variables.- 3 Graphics.- 3.1 The Media.- 3.1.1 The Graphics Window.- 3.1.2 The Driver.- 3.1.3 Global Handling Commands.- 3.2 Global Plot Parameters.- 3.2.1 Graphical Context.- 3.2.2 Indirect Manipulation of the Graphics Context.- 3.3 2-D Plotting.- 3.3.1 Basic Syntax for 2-D Plots.- 3.3.2 Specialized 2-D Plotting Functions.- 3.3.3 Captions and Presentation.- 3.3.4 Plotting Geometric Figures.- 3.3.5 Some Graphics Functions for Automatic Control.- 3.3.6 Interactive Graphics Utilities.- 3.4 3-D Plotting.- 3.4.1 3-D Plotting.- 3.4.2 Specialized 3-D Plots and Tools.- 3.4.3 Mixing 2-D and 3-D Graphics.- 3.5 Examples.- 3.5.1 Subwindows.- 3.5.2 A Set of Figures.- 3.6 Printing Graphics and Exporting to LATEX.- 3.6.1 Window to Printer.- 3.6.2 Creating a Postscript File.- 3.6.3 Including a Postscript File in LATEX.- 3.6.4 Scilab, Xfig, and Postscript.- 3.6.5 Creating Encapsulated Postscript Files.- 4 A Tour of Some Basic Functions.- 4.1 Linear Algebra.- 4.1.1 QR Factorization.- 4.1.2 Singular Value Decomposition.- 4.1.3 Schur Form and Eigenvalues.- 4.1.4 Block Diagonalization and Eigenvectors.- 4.1.5 Fine Structure.- 4.1.6 Subspaces.- 4.2 Polynomial and Rational Function Manipulation.- 4.2.1 General Purpose Functions.- 4.2.2 Matrix Pencils.- 4.3 Sparse Matrices.- 4.4 Random Numbers.- 4.5 Cumulative Distribution Functions and Their Inverses.- 5 Advanced Programming.- 5.1 Functions and Primitives.- 5.2 The Call Function.- 5.3 Building Interface Programs.- 5.4 Accessing “Global” Variables Within a Wrapper.- 5.4.1 Stack Handling Functions.- 5.4.2 Functional Arguments.- 5.5 Intersci.- 5.5.1 A First Intersci Example.- 5.5.2 Intersci Descriptor File Syntax.- 5.6 Dynamic Linking.- 5.7 Static Linking.- 5.7.1 Static Linking of an Interface.- 5.7.2 Functional Argument: Static Linking.- II Tools.- 6 Systems and Control Toolbox.- 6.1 Linear Systems.- 6.1.1 State-Space Representation.- 6.1.2 Transfer-Matrix Representation.- 6.2 System Definition.- 6.2.1 Interconnected Systems.- 6.2.2 Linear Fractional Transformation (LFT).- 6.2.3 Time Discretization.- 6.3 Improper Systems.- 6.3.1 Scilab Representation.- 6.3.2 Scilab Implementation.- 6.4 System Operations.- 6.4.1 Pole-Zero Calculations.- 6.4.2 Controllability and Pole Placement.- 6.4.3 Observability and Observers.- 6.5 Control Tools.- 6.6 Classical Control.- 6.6.1 Frequency Response Plots.- 6.7 State-Space Control.- 6.7.1 Augmenting the Plant.- 6.7.2 Standard Problem.- 6.7.3 LQG Design.- 6.7.4 Scilab Tools for Controller Design.- 6.8 H? Control.- 6.9 Model Reduction.- 6.10 Identification.- 6.11 Linear Matrix Inequalities.- 7 Signal Processing.- 7.1 Time and Frequency Representation of Signals.- 7.1.1 Resampling Signals.- 7.1.2 The DFT and the FFT.- 7.1.3 Transfer Function Representation of Signals.- 7.1.4 State-Space Representation.- 7.1.5 Changing System Representation.- 7.1.6 Frequency-Response Evaluation.- 7.1.7 The Chirp z-Transform.- 7.2 Filtering and Filter Design.- 7.2.1 Filtering.- 7.2.2 Finite Impulse Response Filter Design.- 7.2.3 Infinite Impulse Response Filter Design.- 7.3 Spectral Estimation.- 7.3.1 The Modified Periodogram Method.- 7.3.2 The Correlation Method.- 8 Simulation and Optimization Tools.- 8.1 Models.- 8.2 Integrating ODEs.- 8.2.1 Calling ode.- 8.2.2 Choosing Between Methods.- 8.2.3 ODE Integration with Stopping Times.- 8.2.4 Sampled Systems.- 8.3 Integrating DAEs.- 8.3.1 Implicit Linear ODEs.- 8.3.2 General DAEs.- 8.3.3 DAEs with Stopping Time.- 8.4 Solving Optimization Problems.- 8.4.1 Quadratic Optimization.- 8.4.2 General Optimization.- 8.4.3 Solving Systems of Equations.- 9 SCICOS — A Dynamical System Builder and Simulator.- 9.1 Hybrid System Formalism.- 9.2 Getting Started.- 9.2.1 Constructing a Simple Model.- 9.2.2 Model Simulation.- 9.2.3 Symbolic Parameters and “Context”.- 9.2.4 Use of Super Block.- 9.2.5 Simulation Outside the Scicos Environment.- 9.3 Basic Concepts.- 9.3.1 Basic Blocks.- 9.3.2 Inheritance and Time Dependence.- 9.3.3 Synchronization.- 9.4 Block Construction.- 9.4.1 Super Block.- 9.4.2 Scifunc Block.- 9.4.3 GENERIC Block.- 9.4.4 Fortran Block and C Blocks.- 9.4.5 Interfacing Function.- 9.4.6 Computational Function.- 9.5 Example.- 9.6 Palettes.- 9.6.1 Existing Palettes.- 9.6.2 Constructing New Palettes.- 10 Symbolic/Numeric Environment.- 10.1 Introduction.- 10.2 Generating Optimized Fortran Code with Maple.- 10.3 Maple to Scilab Interface.- 10.4 First Example: Simulation of a Rolling Wheel.- 10.5 Second Example: Control of an n-Link Pendulum.- 10.5.1 Simulation of the n-Link Pendulum.- 10.5.2 Control of the n-Link Pendulum.- 11 Graph and Network Toolbox: Metanet.- 11.1 What Is a Graph?.- 11.2 Representation of Graphs.- 11.2.1 Standard Tail/Head Representation.- 11.2.2 Other Representations.- 11.2.3 Graphs and Sparse Matrices.- 11.3 Creating and Loading Graphs.- 11.3.1 Creating Graphs.- 11.3.2 Loading and Saving Graphs.- 11.3.3 Using the Metanet Window.- 11.4 Generating Graphs and Networks.- 11.5 Graph and Network Computations.- 11.5.1 Getting Information About Graphs.- 11.5.2 Paths and Nodes.- 11.5.3 Modifying Graphs.- 11.5.4 Creating New Graphs From Old Ones.- 11.5.5 Graph Problem Solving.- 11.5.6 Network Flows.- 11.5.7 The Pipe Network Problem.- 11.5.8 Other Computations.- 11.6 Examples Using Metanet.- 11.6.1 Routing in the Paris Metro.- 11.6.2 Praxitele Transportation System.- III Applications.- 12 Modal Identification of a Mechanical Structure.- 12.1 Modeling the System.- 12.2 Modeling the Excitation.- 12.2.1 Decomposition of the Unknown Input.- 12.2.2 Contribution of the Colored Noise.- 12.2.3 Contribution of the Harmonics.- 12.2.4 The Final Discrete-State Model.- 12.3 State-Space Representation and an ARMA Model.- 12.4 Modal Identification.- 12.4.1 Instrumental Variable Method.- 12.4.2 Balanced Realization Method.- 12.5 Numerical Experiments.- 12.5.1 Basic Computations.- 12.5.2 Some Plots of Results.- 13 Control of Hydraulic Equipment in a River Valley.- 13.1 Introduction.- 13.2 Description of a Managed River Valley.- 13.2.1 Hydraulic Equipment in a River Valley.- 13.2.2 Power Production.- 13.2.3 Structural Analysis.- 13.2.4 Controller Structure.- 13.2.5 Central Hydraulic Supervision Station.- 13.2.6 Local Controllers.- 13.3 Race Modeling.- 13.3.1 Physical Description.- 13.3.2 Mathematical Model.- 13.3.3 Race Numerical Simulation.- 13.4 Choice of Observation.- 13.4.1 Volume Observer.- 13.4.2 Level Observer.- 13.5 Control of a Race.- 13.5.1 Race Dynamics Identification.- 13.5.2 Local Control Synthesis.- 13.5.3 Series Anticipations Design.- 13.5.4 Parallel Anticipation Design.- 13.5.5 Feedback Controller Design.- 13.6 Metalido Overview.- 13.6.1 Graphical User Interface.- 13.6.2 Scicos.- 13.6.3 Data Structures.
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