Using This Guide Using This Guide System Identification is about building mathematical models of dynamic systems based on measured data. Some knowledge about such models is therefore necessary for successful use of the toolbox. The topic is treated in several places in Chapter 3, Tutorial"and there is a wide range of dynamic models For review of basic knowledge, see"How do lge or se textbooks available for introductory and in-depth studies. For basic of the toolbox, it is sufficient to have quite superficial insights abou started? "on page 1-3 If you are a beginner, browse through Chapter 2, " The Graphical User Interface"and try out a couple of the data sets that come with the toolbox. Use the graphical user interface(GUI)and check out the built-in help functions to understand what you are doing
Using This Guide xi Using This Guide System Identification is about building mathematical models of dynamic systems based on measured data. Some knowledge about such models is therefore necessary for successful use of the toolbox.The topic is treated in several places in Chapter 3, “Tutorial” and there is a wide range of textbooks available for introductory and in-depth studies. For basic use of the toolbox, it is sufficient to have quite superficial insights about dynamic models. For review of basic knowledge, see “How do I get started?” on page 1-3. If you are a beginner, browse through Chapter 2, “The Graphical User Interface” and try out a couple of the data sets that come with the toolbox. Use the graphical user interface (GUI) and check out the built-in help functions to understand what you are doing
Typographical Conventions We use some or all of these conventions in our manuals Item Convention to use Example Example code font To assign the value 5 to A, Function names/syntax Monospace font The cos function finds the each array element Syntax line example is MLGet var M var name Keys Boldface with an initial Press the Return key. capital letter Literal strings(in syntax Monospace bold fo f freespace(n, whole) descriptions in Reference literals chapters Mathematical Variables in italics This vector represents the expressions Functions, operators, and polynomial constants in standard text. p=x+ 2x+3 MATLAB output Monospace font MATLAB responds wit. Menu names, menu items, and Boldface with an initial Choose the File menu capital letter New terms An array is an ordered collection of information String variables(from a finite monospace italics sysc d2c(sys,'method list)
Preface xii Typographical Conventions We use some or all of these conventions in our manuals. Item Convention to Use Example Example code Monospace font To assign the value 5 to A, enter A = 5 Function names/syntax Monospace font The cos function finds the cosine of each array element. Syntax line example is MLGetVar ML_var_name Keys Boldface with an initial capital letter Press the Return key. Literal strings (in syntax descriptions in Reference chapters) Monospace bold for literals. f = freqspace(n,'whole') Mathematical expressions Variables in italics Functions, operators, and constants in standard text. This vector represents the polynomial p = x2 + 2x + 3 MATLAB output Monospace font MATLAB responds with A = 5 Menu names, menu items, and controls Boldface with an initial capital letter Choose the File menu. New terms Italics An array is an ordered collection of information. String variables (from a finite list) Monospace italics sysc = d2c(sysd, 'method')
Related products Related products The MathWorks provides several products that are especially relevant to the kinds of tasks you can perform with the System Identification Toolbox. In particular, the Systems Identification Toolbox requires these products MATLAB For more information about any of these products, see either: The online documentation for that product, if it is installed or if you are eading the documentation from the cD TheMathworksWebsiteathttp://www.mathworks.comseethe s sector Note The products listed below complement the functionality of the System Identification toolbox Product Description Simulink Interactive, graphical environment for modeling, simulating, and prototyping dynamic systems Control System Toolbox Tool for modeling, analyzing, and designing control systems using classical and modern techniques Data Acquisition Toolbox MATLAB functions for direct access to live, measured data from MATLAB Financial Time Series Tool for analyzing time series data in the financial markets toolbox Financial Toolbox MATLAB functions for quantitative financial modeling and analytic prototyping Fuzzy Logic Toolbox Tool to help master fuzzy logic techniques and their application te practical control problems
Related Products xiii Related Products The MathWorks provides several products that are especially relevant to the kinds of tasks you can perform with the System Identification Toolbox. In particular, the Systems Identification Toolbox requires these products: • MATLAB® For more information about any of these products, see either: • The online documentation for that product, if it is installed or if you are reading the documentation from the CD • The MathWorks Web site, at http://www.mathworks.com; see the “products” section Note The products listed below complement the functionality of the System Identification toolbox. Product Description Simulink® Interactive, graphical environment for modeling, simulating, and prototyping dynamic systems Control System Toolbox Tool for modeling, analyzing, and designing control systems using classical and modern techniques Data Acquisition Toolbox MATLAB functions for direct access to live, measured data from MATLAB Financial Time Series Toolbox Tool for analyzing time series data in the financial markets Financial Toolbox MATLAB functions for quantitative financial modeling and analytic prototyping Fuzzy Logic Toolbox Tool to help master fuzzy logic techniques and their application to practical control problems
Product Description AAnalysis and Synthesis Computational algorithms for the structured singular value, u, applicable to robustness and performance analysis for systems with odeling and parameter uncertainties Neural Network Toolbox Comprehensive environment for neural network research, design nd simulation within MATLaB Optimization Toolbox Tool for general and large-scale optimization of nonlinear problems, as well as for linear programming, quadratic programming, nonlinear least squares, and solving nonlinear equations Robust Control Toolbox Tools for modeling, analysis, and design of "robust" multivariable feedback control systems using Ho techniques Signal Processing Tool for algorithm development, signal and linear system analysis, nd time-series data modeling Statistics Toolbox Tool for analyzing historical data, modeling systems, developing statistical algorithms, and learning and teaching statistics
Preface xiv -Analysis and Synthesis Toolbox Computational algorithms for the structured singular value, µ, applicable to robustness and performance analysis for systems with modeling and parameter uncertainties Neural Network Toolbox Comprehensive environment for neural network research, design, and simulation within MATLAB Optimization Toolbox Tool for general and large-scale optimization of nonlinear problems, as well as for linear programming, quadratic programming, nonlinear least squares, and solving nonlinear equations Robust Control Toolbox Tools for modeling, analysis, and design of “robust” multivariable feedback control systems using H∞ techniques Signal Processing Toolbox Tool for algorithm development, signal and linear system analysis, and time-series data modeling Statistics Toolbox Tool for analyzing historical data, modeling systems, developing statistical algorithms, and learning and teaching statistics Product Description
About the author About the author Lennart Ljung received his PhD in Automatic Control from Lund Institute of Technology in 1974. Since 1976 he is Professor of the chair of Automatic Control in Linkoping, Sweden, and is currently Director of the Center for the "Information Systems for Industrial Control and Supervision"(ISIS). He has held visiting positions at Stanford and MIT and has written several books on System Identification and Estimation He is an IEEE Fellow, an IFAC Advisor, a member of the Royal Swedish Academy of Sciences(KvA)and of the Royal Swedish Academy of Engineering Sciences (IVA), and has received honorary doctorates from the Baltic State Technical University in St Petersburg, and from Uppsala University
About the Author xv About the Author Lennart Ljung received his PhD in Automatic Control from Lund Institute of Technology in 1974. Since 1976 he is Professor of the chair of Automatic Control in Linkoping, Sweden, and is currently Director of the Center for the “Information Systems for Industrial Control and Supervision” (ISIS). He has held visiting positions at Stanford and MIT and has written several books on System Identification and Estimation. He is an IEEE Fellow, an IFAC Advisor, a member of the Royal Swedish Academy of Sciences (KVA) and of the Royal Swedish Academy of Engineering Sciences (IVA), and has received honorary doctorates from the Baltic State Technical University in St Petersburg, and from Uppsala University