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Applied Control Systems Design
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036aXA-GB
037beng
077a392096501 Buchausg. u.d.T.: ‡Mahmoud, Magdi S., 1948 - : Applied control systems design
087q978-1-4471-2878-6
100 Mahmoud, Magdi S.
104bXia, Yuanqing
331 Applied Control Systems Design
410 London
412 Springer London
425 2012
425a2012
433 Online-Ressource (XXI, 560p. 363 illus., 216 illus. in color, digital)
451bSpringerLink. Bücher
501 Description based upon print version of record
517 Applied Control Systems Design; Preface; Acknowledgements; Contents; List of Notations; Chapter 1: Introduction; 1.1 Overview; 1.2 Modern Automation Structure; 1.3 Systems Identification; 1.4 Control Design; 1.5 Outline of the Book; 1.5.1 Methodology; 1.5.2 Chapter Organization; References; Chapter 2: Some Industrial Systems; 2.1 Introduction; 2.2 Steam Generation Unit; 2.2.1 System Dynamics; 2.3 Small-Power Wind Turbine; 2.3.1 Wind Turbine Basics; 2.4 Unmanned Surface Marine Vehicle; 2.4.1 Dynamic Model; 2.5 Industrial Evaporation Unit; 2.5.1 Mathematical Models. 2.5.2 Multistage Evaporator System2.6 Distillation Tower; 2.6.1 A Particular Tower; 2.7 Falling Film Evaporator; 2.7.1 A Single Effect Evaporator; 2.8 Vapor Compression Cycle Systems; 2.8.1 A Typical System; 2.9 Flutter of an Aircraft F-18; 2.9.1 Flutter Input and Output Data; 2.10 A Hydraulic Pumping System; 2.10.1 Hydraulic Process and the Data; 2.10.2 Static Behavior; 2.11 Notes and References; References; Chapter 3: System Identification Methods; 3.1 Introduction; 3.2 Parameter Estimation Approach; Model Validation; 3.2.1 Estimation Algorithms; 3.2.2 Gradient Algorithm. 3.2.3 Least Squares Algorithm3.2.4 Choice of the Adaptation Gain; Choice of the Initial Adaptation Gain F(0); 3.3 Transfer-Function Methods; 3.3.1 Prediction Error Method (PEM); 3.4 Subspace Identification Method; 3.4.1 State Space Models; 3.4.2 Block Hankel Matrices and State Sequences; 3.4.3 Model Matrices; 3.4.4 Orthogonal Projections; 3.4.5 Oblique Projections; 3.4.6 Deterministic Subspace Identification; Calculation of a State Sequence; Computing the System Matrices; 3.4.7 Stochastic Subspace Identification; Calculation of a State Sequence; Computing the System Matrices. 3.4.8 Combined Deterministic-Stochastic AlgorithmCalculation of a State Sequence; Computing the System Matrices; 3.4.9 Variations; 3.5 Output-Error Parametric Model Identification; 3.5.1 Introduction; 3.5.2 Problems in Estimating Parameters; 3.5.3 Identification Example 3.1; 3.5.4 Parameterizing a MIMO Model; 3.5.5 Identification Example 3.2; 3.5.6 Identification Example 3.3; 3.5.7 Identification Example 3.4; 3.5.8 The Output Normal Form; 3.5.9 Identification Example 3.5; 3.5.10 The Tridiagonal Form; 3.5.11 The Output-Error Cost Function; 3.5.12 Identification Example 3.6. 3.5.13 Numerical Parameter Estimation3.5.14 The Gauss-Newton Method; 3.5.15 Identification Example 3.7; 3.5.16 Regularization in the Gauss-Newton Method; 3.5.17 The Steepest Descent Method; 3.5.18 Gradient Projection; 3.5.19 Analyzing the Accuracy of the Estimates; 3.5.20 Dealing with Colored Measurement Noise; 3.5.21 Identification Example 3.8; 3.5.22 Weighted Least Squares; 3.5.23 Prediction-Error Methods; 3.6 Prediction-Error Parametric Model Estimation; 3.6.1 Introduction; 3.6.2 Prediction-Error Methods; 3.6.3 Parameterizing an Innovation State-Space Model. 3.6.4 The Prediction-Error Cost Function
527 Buchausg. u.d.T.: ‡Mahmoud, Magdi S., 1948 - : Applied control systems design
540aISBN 978-1-4471-2879-3
700 |54.83
700 |TJFM
700 |TEC004000
700 |*93-02
700 |62M20
700 |62F99
700 |62J05
700 |62P30
700 |93B30
700 |93B51
700 |93C40
700 |93C95
700 |93E12
700 |93E20
700b|629.8
700c|TJ212-225
750 Yuanqing Xia
753 Applied Control System Design examines several methods for building up systems models based on real experimental data from typical industrial processes and incorporating system identification techniques. The text takes a comparative approach to the models derived in this way judging their suitability for use in different systems and under different operational circumstances. A broad spectrum of control methods including various forms of filtering, feedback and feedforward control is applied to the models and the guidelines derived from the closed-loop responses are then composed into a concrete self-tested recipe to serve as a check-list for industrial engineers or control designers. System identification and control design are given equal weight in model derivation and testing to reflect their equality of importance in the proper design and optimization of high-performance control systems. Readers' assimilation of the material discussed is assisted by the provision of problems and examples. Most of these exercises use MATLAB® to make computation and visualization more straightforward. Applied Control System Design will be of interest to academic researchers for its comparison of different systems models and their response to different control methods and will assist graduate students in learning the practical necessities of advanced control system design. The consistent reference to real systems coupled with self-learning tools will assist control practitioners who wish to keep up to date with the latest control design ideas.
902s 209202319 Regelungstechnik
012 365267457
081 Mahmoud, Magdi S.: Applied Control Systems Design
100 Springer E-Book
125aElektronischer Volltext - Campuslizenz
655e$uhttp://dx.doi.org/10.1007/978-1-4471-2879-3
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