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| system identification toolbox: System Identification Lennart Ljung, 1998-12-29 The field's leading text, now completely updated. Modeling dynamical systems — theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung combines careful mathematics, a practical understanding of real-world applications, and extensive exercises. He introduces both black-box and tailor-made models of linear as well as non-linear systems, and he describes principles, properties, and algorithms for a variety of identification techniques: Nonparametric time-domain and frequency-domain methods. Parameter estimation methods in a general prediction error setting. Frequency domain data and frequency domain interpretations. Asymptotic analysis of parameter estimates. Linear regressions, iterative search methods, and other ways to compute estimates. Recursive (adaptive) estimation techniques. Ljung also presents detailed coverage of the key issues that can make or break system identification projects, such as defining objectives, designing experiments, controlling the bias distribution of transfer-function estimates, and carefully validating the resulting models. The first edition of System Identification has been the field's most widely cited reference for over a decade. This new edition will be the new text of choice for anyone concerned with system identification theory and practice. |
| system identification toolbox: Basic System Identification with MATLAB Kendall T., 2016-10-27 System Identification Toolbox constructs mathematical models of dynamic systems from measured input-output data. It provides MATLAB(r) functions, Simulink blocks, and an interactive tool for creating and using models of dynamic systems not easily modeled from first principles or specifications You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process odels, and state-space models. The toolbox provides maximum likelihood, prediction-error minimization (PEM), subspace system identification, and other identification techniques.For nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for prediction of system response and for simulation in Simulink. The toolbox also lets you model time-series data and perform time-series forecasting. The more important content in this book is the next:* Transfer function, process model, and state-space model identification using time-domain and frequency-domain response data* Autoregressive (ARX, ARMAX), Box-Jenkins, and Output-Error model estimation using maximum likelihood, prediction-error minimization(PEM), and subspace system identification techniques * Time-series modeling (AR, ARMA, ARIMA) and forecasting* Identification of nonlinear ARX models and Hammerstein-Weiner models with input-output nonlinearities such as saturation and dead zone* Linear and nonlinear grey-box system identification for estimation of user-defined models* Delay estimation, detrending, filtering, resampling, and reconstruction of missing data |
| system identification toolbox: System Identification Rik Pintelon, Johan Schoukens, 2004-03-22 Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about: * Choice of experimental setup and experiment design * Automatic characterization of disturbing noise * Generation of a good plant model * Detection, qualification, and quantification of nonlinear distortions * Identification of continuous- and discrete-time models * Improved model validation tools and from the theoretical side about: * System identification * Interrelations between time- and frequency-domain approaches * Stochastic properties of the estimators * Stochastic analysis System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout. |
| system identification toolbox: Subspace Identification for Linear Systems Peter van Overschee, B.L. de Moor, 2012-12-06 Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with theapplication of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering. |
| system identification toolbox: Practical Design and Application of Model Predictive Control Nassim Khaled, Bibin Pattel, 2018-05-04 Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the designed MPC controller in a real-time platform such as Arduino®. The selected problems are nonlinear and challenging, and thus serve as an excellent experimental, dynamic system to show the reader the capability of MPC. The step-by-step solutions of the problems are thoroughly documented to allow the reader to easily replicate the results. Furthermore, the MATLAB® and Simulink® codes for the solutions are available for free download. Readers can connect with the authors through the dedicated website which includes additional free resources at www.practicalmpc.com. - Illustrates how to design, tune and deploy MPC for projects in a quick manner - Demonstrates a variety of applications that are solved using MATLAB® and Simulink® - Bridges the gap in providing a number of realistic problems with very hands-on training - Provides MATLAB® and Simulink® code solutions. This includes nonlinear plant models that the reader can use for other projects and research work - Presents application problems with solutions to help reinforce the information learned |
| system identification toolbox: Filtering and System Identification Michel Verhaegen, Vincent Verdult, 2012-07-19 Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127. |
| system identification toolbox: System Identification Toolbox - for Use with MATLAB Lennart Ljung, 1997 The System Identification Toolbox is for building accurate, simplified models of complex systems from noisy time-series data. |
| system identification toolbox: System Identification with MATLAB. Non Linear Models, Odes and Time Series Marvin L., 2016-10-23 In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. Thisb book develops de next task with models: Nonlinear Black-Box Model Identification Nonlinear Model Identification Fit Nonlinear Models Identifying Nonlinear ARX Models Nonlinearity Estimators for Nonlinear ARX Models Estimate Nonlinear ARX Models in the GUI Estimate Nonlinear ARX Models at the Command Line Validating Nonlinear ARX Models Identifying Hammerstein-Wiener Models Nonlinearity Estimators for Hammerstein-Wiener Models Estimation Algorithm for Hammerstein-Wiener Models Validating Hammerstein-Wiener Models Linear Approximation of Nonlinear Black-Box Models ODE Parameter Estimation (Grey-Box Modeling) Estimating Linear Grey-Box Models Estimating Nonlinear Grey-Box Models After Estimating Grey-Box Models Estimating Coefficients of ODEs to Fit Given Solution Estimate Model Using Zero/Pole/Gain Parameters Time Series Identification Estimating Time-Series Power Spectra Estimate Time-Series Power Spectra Using the GUI Estimate Time-Series Power Spectra at the Command Line Estimating AR and ARMA Models Estimating Polynomial Time-Series Models in the GUI Estimating AR and ARMA Models at the Command Line Estimating State-Space Time-Series Models Estimating State-Space Models at the Command Line Identify Time-Series Models at Command Line Estimating Nonlinear Models for Time-Series Data Estimating ARIMA Models Analyzing of Time-Series Models Recursive Model Identification General Form of Recursive Estimation Algorithm Kalman Filter Algorithm Recursive Estimation and Data Segmentation Techniques in System Identification Toolbox Model Analysis Validating Models After Estimation Plotting Models in the GUI Simulating and Predicting Model Output Simulation and Prediction in the GUI Simulation and Prediction at the Command Line Predict Using Time-Series Model Residual Analysis Impulse and Step Response Plots Frequency Response Plots Displaying the Confidence Interval Noise Spectrum Plots Pole and Zero Plots Analyzing MIMO Models Akaike's Criteria for Model Validation Troubleshooting Models Unstable Models Missing Input Variables Complicated Nonlinearities Spectrum Estimation Using Complex Data System Identification Toolbox Blocks Using System Identification Toolbox Blocks in Simulink Models Identifying Linear Models Simulating Identified Model Output in Simulink Simulate Identified Model Using Simulink Software System Identification Tool GUI |
| system identification toolbox: Aircraft System Identification Eugene Morelli, Eugene A. Morelli, Vladislav Klein, 2016 This book provides a comprehensive overview of both the theoretical underpinnings and the practical application of aircraft modeling based on experimental data also known as aircraft system identification. Much of the material presented comes from the authors own extensive research and teaching activities at the NASA Langley Research Center, and is based on real-world applications of system identification to aircraft. The book uses actual flight-test and wind-tunnel data for case studies and examples, and is a valuable resource for researchers and practicing engineers, as well as a textbook for postgraduate and senior-level courses. [...] The methods and algorithms explained in the book are implemented in a NASA software toolbox called SIDPAC (System IDentification Programs for AirCraft). SIDPAC is written in MATLAB®, and is available by request from NASA Langley Research Center. SIDPAC is composed of many different tools that implement a wide variety of approaches explained fully in the book. These tools can be readily applied to solve aircraft system identification problems. |
| system identification toolbox: Nonlinear System Identification Stephen A. Billings, 2013-07-29 Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours A completely new class of filters that can move, split, spread, and focus energy The response spectrum map and the study of sub harmonic and severely nonlinear systems Algorithms that can track rapid time variation in both linear and nonlinear systems The important class of spatio-temporal systems that evolve over both space and time Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems. This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems. |
| system identification toolbox: Linear Feedback Control Dingyu Xue, YangQuan Chen, Derek P. Atherton, 2007-01-01 Less mathematics and more working examples make this textbook suitable for almost any type of user. |
| system identification toolbox: Basic Tutorial on Simulation of Microgrids Control Using MATLAB® & Simulink® Software Flávia de Andrade, Miguel Castilla, Benedito Donizeti Bonatto, 2020-03-03 This book offers a detailed guide to the design and simulation of basic control methods applied to microgrids in various operating modes, using MATLAB® Simulink® software. It includes discussions on the performance of each configuration, as well as the advantages and limitations of the droop control method. The content is organised didactically, with a level of mathematical and scientific rigour suitable for undergraduate and graduate programmes, as well as for industry professionals. The use of MATLAB® Simulink® software facilitates the learning process with regard to modelling and simulating power electronic converters at the interface of distributed energy resource (DER) systems. The book also features a wealth of illustrations, schematics, and simulation results. Given its scope, it will greatly benefit undergraduate and graduate students in the fields of electrical and electronics engineering, as well as professionals working in microgrid design and implementation. |
| system identification toolbox: System Identification with MATLAB. Linear Models Marvin L., 2016-10-23 In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems. This book develops the next tasks with linear models:* Black-Box Modeling * Identifying Frequency-Response Models * Identifying Impulse-Response Models * Identifying Process Models * Identifying Input-Output Polynomial Models * Identifying State-Space Models * Identifying Transfer Function Models * Refining Linear Parametric Models* Refine ARMAX Model with Initial Parameter Guesses at Command Line* Refine Initial ARMAX Model at Command Line * Extracting Numerical Model Data * Transforming Between Discrete-Time and Continuous-Time Representations * Continuous-Discrete Conversion Methods * Effect of Input Intersample Behavior on Continuous-Time Models * Transforming Between Linear Model Representations * Subreferencing Models* Concatenating Models * Merging Models* Building and Estimating Process Models Using System Identification Toolbox* Determining Model Order and Delay 5* Model Structure Selection: Determining Model Order and Input Delay * Frequency Domain Identification: Estimating Models Using Frequency Domain Data * Building Structured and User-Defined Models Using System Identification Toolbox |
| system identification toolbox: MATLAB Control Systems Engineering Cesar Lopez, 2014-09-22 MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. MATLAB Control Systems Engineering introduces you to the MATLAB language with practical hands-on instructions and results, allowing you to quickly achieve your goals. In addition to giving an introduction to the MATLAB environment and MATLAB programming, this book provides all the material needed to design and analyze control systems using MATLAB’s specialized Control Systems Toolbox. The Control Systems Toolbox offers an extensive range of tools for classical and modern control design. Using these tools you can create models of linear time-invariant systems in transfer function, zero-pole-gain or state space format. You can manipulate both discrete-time and continuous-time systems and convert between various representations. You can calculate and graph time response, frequency response and loci of roots. Other functions allow you to perform pole placement, optimal control and estimates. The Control System Toolbox is open and extendible, allowing you to create customized M-files to suit your specific applications. |
| system identification toolbox: Numerical Methods for Unconstrained Optimization and Nonlinear Equations J. E. Dennis, Jr., Robert B. Schnabel, 1996-12-01 This book has become the standard for a complete, state-of-the-art description of the methods for unconstrained optimization and systems of nonlinear equations. Originally published in 1983, it provides information needed to understand both the theory and the practice of these methods and provides pseudocode for the problems. The algorithms covered are all based on Newton's method or quasi-Newton methods, and the heart of the book is the material on computational methods for multidimensional unconstrained optimization and nonlinear equation problems. The republication of this book by SIAM is driven by a continuing demand for specific and sound advice on how to solve real problems. The level of presentation is consistent throughout, with a good mix of examples and theory, making it a valuable text at both the graduate and undergraduate level. It has been praised as excellent for courses with approximately the same name as the book title and would also be useful as a supplemental text for a nonlinear programming or a numerical analysis course. Many exercises are provided to illustrate and develop the ideas in the text. A large appendix provides a mechanism for class projects and a reference for readers who want the details of the algorithms. Practitioners may use this book for self-study and reference. For complete understanding, readers should have a background in calculus and linear algebra. The book does contain background material in multivariable calculus and numerical linear algebra. |
| system identification toolbox: Modeling and Analysis of Dynamic Systems Ramin S. Esfandiari, Bei Lu, 2010-03-23 Using MATLAB® and Simulink® to perform symbolic, graphical, numerical, and simulation tasks, Modeling and Analysis of Dynamic Systems provides a thorough understanding of the mathematical modeling and analysis of dynamic systems. It meticulously covers techniques for modeling dynamic systems, methods of response analysis, and vibration and control systems. After introducing the software and essential mathematical background, the text discusses linearization and different forms of system model representation, such as state-space form and input-output equation. It then explores translational, rotational, mixed mechanical, electrical, electromechanical, pneumatic, liquid-level, and thermal systems. The authors also analyze the time and frequency domains of dynamic systems and describe free and forced vibrations of single and multiple degree-of-freedom systems, vibration suppression, modal analysis, and vibration testing. The final chapter examines aspects of control system analysis, including stability analysis, types of control, root locus analysis, Bode plot, and full-state feedback. With much of the material rigorously classroom tested, this textbook enables undergraduate students to acquire a solid comprehension of the subject. It provides at least one example of each topic, along with multiple worked-out examples for more complex topics. The text also includes many exercises in each chapter to help students learn firsthand how a combination of ideas can be used to analyze a problem. |
| system identification toolbox: Nonlinear System Identification Oliver Nelles, 2020-09-09 This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems. |
| system identification toolbox: Dynamic Systems Biology Modeling and Simulation Joseph DiStefano III, 2015-01-10 Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems – from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial – with clearly spelled-out and unified nomenclature – derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. - Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics - The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of math modeling with life sciences - Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization - Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models - A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course - Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content - The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu |
| system identification toolbox: Principles of System Identification Arun K. Tangirala, 2015 |
| system identification toolbox: Nonlinear System Identification Oliver Nelles, 2001 Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises. |
| system identification toolbox: Modelling and Control of Dynamic Systems Using Gaussian Process Models Juš Kocijan, 2015-11-21 This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. |
| system identification toolbox: Inverse system identification with applications in predistortion Ylva Jung, 2018-12-19 Models are commonly used to simulate events and processes, and can be constructed from measured data using system identification. The common way is to model the system from input to output, but in this thesis we want to obtain the inverse of the system. Power amplifiers (PAs) used in communication devices can be nonlinear, and this causes interference in adjacent transmitting channels. A prefilter, called predistorter, can be used to invert the effects of the PA, such that the combination of predistorter and PA reconstructs an amplified version of the input signal. In this thesis, the predistortion problem has been investigated for outphasing power amplifiers, where the input signal is decomposed into two branches that are amplified separately by highly efficient nonlinear amplifiers and then recombined. We have formulated a model structure describing the imperfections in an outphasing abbrPA and the matching ideal predistorter. The predistorter can be estimated from measured data in different ways. Here, the initially nonconvex optimization problem has been developed into a convex problem. The predistorters have been evaluated in measurements. The goal with the inverse models in this thesis is to use them in cascade with the systems to reconstruct the original input. It is shown that the problems of identifying a model of a preinverse and a postinverse are fundamentally different. It turns out that the true inverse is not necessarily the best one when noise is present, and that other models and structures can lead to better inversion results. To construct a predistorter (for a PA, for example), a model of the inverse is used, and different methods can be used for the estimation. One common method is to estimate a postinverse, and then using it as a preinverse, making it straightforward to try out different model structures. Another is to construct a model of the system and then use it to estimate a preinverse in a second step. This method identifies the inverse in the setup it will be used, but leads to a complicated optimization problem. A third option is to model the forward system and then invert it. This method can be understood using standard identification theory in contrast to the ones above, but the model is tuned for the forward system, not the inverse. Models obtained using the various methods capture different properties of the system, and a more detailed analysis of the methods is presented for linear time-invariant systems and linear approximations of block-oriented systems. The theory is also illustrated in examples. When a preinverse is used, the input to the system will be changed, and typically the input data will be different than the original input. This is why the estimation of preinverses is more complicated than for postinverses, and one set of experimental data is not enough. Here, we have shown that identifying a preinverse in series with the system in repeated experiments can improve the inversion performance. |
| system identification toolbox: System Identification Toolbox 7 Lennart Ljung, 2007 |
| system identification toolbox: System Identification Torsten Söderström, Petre Stoica, 1989 A textbook designed for senior undergraduate and graduate level classroom courses on system identification. Examples and problems. Annotation copyrighted by Book News, Inc., Portland, OR |
| system identification toolbox: Data Acquisition Using LabVIEW Behzad Ehsani, 2016-12-14 Transform physical phenomena into computer-acceptable data using a truly object-oriented language About This Book Create your own data acquisition system independently using LabVIEW and build interactive dashboards Collect data using National Instrument's and third-party, open source, affordable hardware Step-by-step real-world examples using various tools that illustrate the fundamentals of data acquisition Who This Book Is For If you are an engineer, scientist, experienced hobbyist, or student, you will highly benefit from the content and examples illustrated in this book. A working knowledge of precision testing, measurement instruments, and electronics, as well as a background in computer fundamentals and programming is expected. What You Will Learn Create a virtual instrument which highlights common functionality of LabVIEW Get familiarized with common buses such as Serial, GPIB, and SCPI commands Staircase signal acquisition using NI-DAQmx Discover how to measure light intensity and distance Master LabVIEW debugging techniques Build a data acquisition application complete with an installer and required drivers Utilize open source microcontroller Arduino and a 32-bit Arduino compatible Uno32 using LabVIEW programming environment In Detail NI LabVIEW's intuitive graphical interface eliminates the steep learning curve associated with text-based languages such as C or C++. LabVIEW is a proven and powerful integrated development environment to interact with measurement and control hardware, analyze data, publish results, and distribute systems. This hands-on tutorial guide helps you harness the power of LabVIEW for data acquisition. This book begins with a quick introduction to LabVIEW, running through the fundamentals of communication and data collection. Then get to grips with the auto-code generation feature of LabVIEW using its GUI interface. You will learn how to use NI-DAQmax Data acquisition VIs, showing how LabVIEW can be used to appropriate a true physical phenomenon (such as temperature, light, and so on) and convert it to an appropriate data type that can be manipulated and analyzed with a computer. You will also learn how to create Distribution Kit for LabVIEW, acquainting yourself with various debugging techniques offered by LabVIEW to help you in situations where bugs are not letting you run your programs as intended. By the end of the book, you will have a clear idea how to build your own data acquisition system independently and much more. Style and approach A hands-on practical guide that starts by laying down the software and hardware foundations necessary for subsequent data acquisition-intensive chapters. The book is packed full of specific examples with software screenshots and schematic diagrams to guide you through the creation of each virtual instrument. |
| system identification toolbox: Nonlinear System Identification with Local Linear Neuro-fuzzy Models Oliver Nelles, 1999 |
| system identification toolbox: Proceedings of the 1st International Conference on Numerical Modelling in Engineering Magd Abdel Wahab, 2018-08-25 This book contains manuscripts of topics related to numerical modeling in Civil Engineering (Volume 1) as part of the proceedings of the 1st International Conference on Numerical Modeling in Engineering (NME 2018), which was held in the city of Ghent, Belgium. The overall objective of the conference is to bring together international scientists and engineers in academia and industry in fields related to advanced numerical techniques, such as FEM, BEM, IGA, etc., and their applications to a wide range of engineering disciplines. This volume covers industrial engineering applications of numerical simulations to Civil Engineering, including: Bridges and dams, Cyclic loading, Fluid dynamics, Structural mechanics, Geotechnical engineering, Thermal analysis, Reinforced concrete structures, Steel structures, Composite structures. |
| system identification toolbox: Graphical User Interface of System Identification Toolbox for MATLAB. Hiroyuki Takanashi, Shuichi Adachi, 2010 In this chapter, we have described the advantages of using a GUI environment in system identification and the development of a GUI-SITB. The effectiveness of the toolbox was demonstrated by a simple example. We confirmed the operation of the GUI-SITB only on MATLAB for a Windows platform. The GUI-SITB has been developed using MATLAB R13. The GUI-SITB may operate on the latest MATLAB version (R14 or later) with slight modification of the programs. Since evaluation of the identification results is one of the most important parts in system identification, the evaluation method and of the system identification algorithm need to be extended to achieve this. Because the GUI-SITB currently displays results only graphically (as illustrated in Figs. 8-10), it would be desirable to implement numerical evaluation methods, one of which would display parameters of the estimated model in an appropriate format. Furthermore, currently incomplete functions, such as the identification experiments for real plants and control system design, need to be rapidly developed. A part of the MIMO system identification procedure has been realized, but it is not yet complete. |
| system identification toolbox: Applied System Identification Jer-Nan Juang, 1994 System identification is the process of developing or improving a mathematical representation of a physical system using experimental data. Over the past decade, several system identification techniques have been developed within different disciplines. This text/reference brings together the significant advances over the past decade into a single unified source -- with common mathematical notation that will enable readers from a variety of engineering areas -- e.g., aerospace, electrical, civil, and mechanical engineering --to apply system identification to engineering systems. Focuses on the three types of identification in engineering structures -- modal parameter identification; structural-model parameter identification; and control-model identification. For researchers and engineers, students, and teachers in vibrations, controls and system identification. |
| system identification toolbox: Applications in Control Ivo Petráš, 2019-02-19 This multi-volume handbook is the most up-to-date and comprehensive reference work in the field of fractional calculus and its numerous applications. This sixth volume collects authoritative chapters covering several applications of fractional calculus in control theory, including fractional controllers, design methods and toolboxes, and a large number of engineering applications of control. |
| system identification toolbox: The 12 Days of Valentine's Jenna Lettice, 2017-12-26 Count the 12 days leading up to Valentine’s Day with this fun-filled picture book inspired by “The 12 Days of Christmas”—perfect for fans of Natasha Wing’s “The Night Before” series. The first day of Valentine’s starts with ONE warm, fuzzy hug. On the second day, the crafts begin with TWO cups of sparkles. On the third day, let’s make our cards with THREE pink pens! Each of the 12 busy days leading up to Valentine’s Day are celebrated in this cumulative rhyming storybook based on “The 12 Days of Christmas.” Kids will love spotting all the fun ways a family gets ready for Valentine’s Day! Also available: The 12 Days of Kindergarten, The 12 Days of Halloween, and The 12 Days of Preschool. will be a fast favorite with the preschool and kindergarten sets.—ReadBrightly.com |
| system identification toolbox: Ten on the Sled Kim Norman, 2011-06-28 Author Kim Norman (Crocodaddy) and illustrator Liza Woodruff have whipped up a rollicking, jolly, snow-filled adventure! In the land of the midnight sun, all the animals are having fun speeding down the hill on Caribous sled. But as they go faster and faster, Seal, Hare, Walrus, and the others all fall off…until just Caribous left, only and lonely. Now, a reindeer likes flying-but never alone, so…one through ten, all leap on again! An ideal picture book for reading-and singing along with-over and over. |
| system identification toolbox: Advanced UAV Aerodynamics, Flight Stability and Control Pascual Marqués, Andrea Da Ronch, 2017-07-11 Comprehensively covers emerging aerospace technologies Advanced UAV aerodynamics, flight stability and control: Novel concepts, theory and applications presents emerging aerospace technologies in the rapidly growing field of unmanned aircraft engineering. Leading scientists, researchers and inventors describe the findings and innovations accomplished in current research programs and industry applications throughout the world. Topics included cover a wide range of new aerodynamics concepts and their applications for real world fixed-wing (airplanes), rotary wing (helicopter) and quad-rotor aircraft. The book begins with two introductory chapters that address fundamental principles of aerodynamics and flight stability and form a knowledge base for the student of Aerospace Engineering. The book then covers aerodynamics of fixed wing, rotary wing and hybrid unmanned aircraft, before introducing aspects of aircraft flight stability and control. Key features: Sound technical level and inclusion of high-quality experimental and numerical data. Direct application of the aerodynamic technologies and flight stability and control principles described in the book in the development of real-world novel unmanned aircraft concepts. Written by world-class academics, engineers, researchers and inventors from prestigious institutions and industry. The book provides up-to-date information in the field of Aerospace Engineering for university students and lecturers, aerodynamics researchers, aerospace engineers, aircraft designers and manufacturers. |
| system identification toolbox: United States Code United States, 2008 The United States Code is the official codification of the general and permanent laws of the United States of America. The Code was first published in 1926, and a new edition of the code has been published every six years since 1934. The 2012 edition of the Code incorporates laws enacted through the One Hundred Twelfth Congress, Second Session, the last of which was signed by the President on January 15, 2013. It does not include laws of the One Hundred Thirteenth Congress, First Session, enacted between January 2, 2013, the date it convened, and January 15, 2013. By statutory authority this edition may be cited U.S.C. 2012 ed. As adopted in 1926, the Code established prima facie the general and permanent laws of the United States. The underlying statutes reprinted in the Code remained in effect and controlled over the Code in case of any discrepancy. In 1947, Congress began enacting individual titles of the Code into positive law. When a title is enacted into positive law, the underlying statutes are repealed and the title then becomes legal evidence of the law. Currently, 26 of the 51 titles in the Code have been so enacted. These are identified in the table of titles near the beginning of each volume. The Law Revision Counsel of the House of Representatives continues to prepare legislation pursuant to 2 U.S.C. 285b to enact the remainder of the Code, on a title-by-title basis, into positive law. The 2012 edition of the Code was prepared and published under the supervision of Ralph V. Seep, Law Revision Counsel. Grateful acknowledgment is made of the contributions by all who helped in this work, particularly the staffs of the Office of the Law Revision Counsel and the Government Printing Office--Preface. |
| system identification toolbox: Flight Test System Identification Roger Larsson, 2019-05-15 With the demand for more advanced fighter aircraft, relying on unstable flight mechanical characteristics to gain flight performance, more focus has been put on model-based system engineering to help with the design work. The flight control system design is one important part that relies on this modeling. Therefore, it has become more important to develop flight mechanical models that are highly accurate in the whole flight envelope. For today’s modern fighter aircraft, the basic flight mechanical characteristics change between linear and nonlinear as well as stable and unstable as an effect of the desired capability of advanced maneuvering at subsonic, transonic and supersonic speeds. This thesis combines the subject of system identification, which is the art of building mathematical models of dynamical systems based on measurements, with aeronautical engineering in order to find methods for identifying flight mechanical characteristics. Here, some challenging aeronautical identification problems, estimating model parameters from flight-testing, are treated. Two aspects are considered. The first is online identification during flight-testing with the intent to aid the engineers in the analysis process when looking at the flight mechanical characteristics. This will also ensure that enough information is available in the resulting test data for post-flight analysis. Here, a frequency domain method is used. An existing method has been developed further by including an Instrumental Variable approach to take care of noisy data including atmospheric turbulence and by a sensor-fusion step to handle varying excitation during an experiment. The method treats linear systems that can be both stable and unstable working under feedback control. An experiment has been performed on a radio-controlled demonstrator aircraft. For this, multisine input signals have been designed and the results show that it is possible to perform more time-efficient flight-testing compared with standard input signals. The other aspect is post-flight identification of nonlinear characteristics. Here the properties of a parameterized observer approach, using a prediction-error method, are investigated. This approach is compared with four other methods for some test cases. It is shown that this parameterized observer approach is the most robust one with respect to noise disturbances and initial offsets. Another attractive property is that no user parameters have to be tuned by the engineers in order to get the best performance. All methods in this thesis have been validated on simulated data where the system is known, and have also been tested on real flight test data. Both of the investigated approaches show promising results. |
| system identification toolbox: Digital Spectral Analysis S. Lawrence Marple, Jr., 2019-03-20 Digital Spectral Analysis offers a broad perspective of spectral estimation techniques and their implementation. Coverage includes spectral estimation of discrete-time or discrete-space sequences derived by sampling continuous-time or continuous-space signals. The treatment emphasizes the behavior of each spectral estimator for short data records and provides over 40 techniques described and available as implemented MATLAB functions. In addition to summarizing classical spectral estimation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. Topics include Prony's method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and two-dimensional methods. Suitable for advanced undergraduates and graduate students of electrical engineering — and for scientific use in the signal processing application community outside of universities — the treatment's prerequisites include some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear algebra. 1987 edition. |
| system identification toolbox: System Identification 2003 Paul Van Den Hof, Bo Wahlberg, Siep Weiland, 2004-06-29 The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems*Provides the latest research on System Identification*Contains contributions written by experts in the field*Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering. |
| system identification toolbox: System Identification Toolbox 7 Lennart Ljung, 2008 |
| system identification toolbox: Getting Started with System Identification Toolbox 7 Lennart Ljung, 2007 |
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