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Identification of Physical Systems

Applications to Condition Monitoring, Fault Diagnosis, Soft Sensor and Controller Design
Wiley-VCH GmbH
ISBN/EAN: 9781119990123
Umbreit-Nr.: 5672856

Sprache: Englisch
Umfang: 224 S.
Format in cm:
Einband: gebundenes Buch

Erschienen am 13.05.2014
Auflage: 1/2014
€ 132,00
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  • Zusatztext

    • Identification of a physical system deals with the problem of identifying its mathematical model using the measured input and output data. As the physical system is generally complex, nonlinear, and its input-output data is corrupted noise, there are fundamental theoretical and practical issues that need to be considered. Identification of Physical Systems addresses this need, presenting a systematic, unified approach to the problem of physical system identification and its practical applications. Starting with a least-squares method, the authors develop various schemes to address the issues of accuracy, variation in the operating regimes, closed loop, and interconnected subsystems. Also presented is a non-parametric signal or data-based scheme to identify a means to provide a quick macroscopic picture of the system to complement the precise microscopic picture given by the parametric model-based scheme. Finally, a sequential integration of totally different schemes, such as non-parametric, Kalman filter, and parametric model, is developed to meet the speed and accuracy requirement of mission-critical systems. Key features: * Provides a clear understanding of theoretical and practical issues in identification and its applications, enabling the reader to grasp a clear understanding of the theory and apply it to practical problems * Offers a self-contained guide by including the background necessary to understand this interdisciplinary subject * Includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book Identification of Physical Systems is a comprehensive reference for researchers and practitioners working in this field and is also a useful source of information for graduate students in electrical, computer, biomedical, chemical, and mechanical engineering.

  • Kurztext

    • Identification of a physical system deals with the problem of identifying its mathematical model using the measured input and output data. As the physical system is generally complex, nonlinear, and its input-output data is corrupted noise, there are fundamental theoretical and practical issues that need to be considered. Identification of Physical Systems addresses this need, presenting a systematic, unified approach to the problem of physical system identification and its practical applications. Starting with a least-squares method, the authors develop various schemes to address the issues of accuracy, variation in the operating regimes, closed loop, and interconnected subsystems. Also presented is a non-parametric signal or data-based scheme to identify a means to provide a quick macroscopic picture of the system to complement the precise microscopic picture given by the parametric model-based scheme. Finally, a sequential integration of totally different schemes, such as non-parametric, Kalman filter, and parametric model, is developed to meet the speed and accuracy requirement of mission-critical systems. Key features: * Provides a clear understanding of theoretical and practical issues in identification and its applications, enabling the reader to grasp a clear understanding of the theory and apply it to practical problems * Offers a self-contained guide by including the background necessary to understand this interdisciplinary subject * Includes case studies for the application of identification on physical laboratory scale systems, as well as number of illustrative examples throughout the book Identification of Physical Systems is a comprehensive reference for researchers and practitioners working in this field and is also a useful source of information for graduate students in electrical, computer, biomedical, chemical, and mechanical engineering.

  • Autorenportrait
    • InhaltsangabePreface xv Nomenclature xxi 1 Modeling of Signals and Systems 1 1.1 Introduction 1 1.2 Classification of Signals 2 1.3 Model of Systems and Signals 5 1.4 Equivalence of Input-Output and State-Space Models 8 1.5 Deterministic Signals 11 1.6 Introduction to Random Signals 23 1.7 Model of Random Signals 28 1.8 Model of a System with Disturbance and Measurement Noise 41 1.9 Summary 50 References 54 Further Readings 54 2 Characterization of Signals: Correlation and Spectral Density 57 2.1 Introduction 57 2.2 Definitions of Auto- and Cross-Correlation (and Covariance) 58 2.3 Spectral Density: Correlation in the Frequency Domain 67 2.4 Coherence Spectrum 74 2.5 Illustrative Examples in Correlation and Spectral Density 76 2.6 InputOutput Correlation and Spectral Density 91 2.7 Illustrative Examples: Modeling and Identification 98 2.8 Summary 109 2.9 Appendix 112 References 116 3 Estimation Theory 117 3.1 Overview 117 3.2 Map Relating Measurement and the Parameter 119 3.3 Properties of Estimators 123 3.4 CramérRao Inequality 127 3.5 Maximum Likelihood Estimation 139 3.6 Summary 154 3.7 Appendix: Cauchy-Schwarz Inequality 157 3.8 Appendix: Cram´er-Rao Lower Bound 157 3.9 Appendix: Fisher Information: Cauchy PDF 161 3.10 Appendix: Fisher Information for i.i.d. PDF 161 3.11 Appendix: Projection Operator 162 3.12 Appendix: Fisher Information: Part Gauss-Part Laplace 164 Problem 165 References 165 Further Readings 165 4 Estimation of Random Parameter 167 4.1 Overview 167 4.2 Minimum Mean-Squares Estimator (MMSE): Scalar Case 167 4.3 MMSE Estimator: Vector Case 169 4.4 Expression for Conditional Mean 172 4.5 Summary 183 4.6 Appendix: Non-Gaussian Measurement PDF 184 References 188 Further Readings 188 5 Linear Least-Squares Estimation 189 5.1 Overview 189 5.2 Linear Least-Squares Approach 189 5.3 Performance of the Least-Squares Estimator 195 5.4 Illustrative Examples 205 5.5 Cram´erRao Lower Bound 209 5.6 Maximum Likelihood Estimation 210 5.7 LeastSquares Solution of UnderDetermined System 212 5.8 Singular Value Decomposition 213 5.9 Summary 218 5.10 Appendix: Properties of the Pseudo-Inverse and the Projection Operator 221 5.11 Appendix: Positive Definite Matrices 222 5.12 Appendix: Singular Value Decomposition of a Matrix 223 5.13 Appendix: Least-Squares Solution for Under-Determined System 228 5.14 Appendix: Computation of Least-Squares Estimate Using the SVD 229 References 229 Further Readings 230 6 Kalman Filter 231 6.1 Overview 231 6.2 Mathematical Model of the System 233 6.3 Internal Model Principle 236 6.4 Duality Between Controller and an Estimator Design 244 6.5 Observer: Estimator for the States of a System 246 6.6 Kalman Filter: Estimator of the States of a Stochastic System 250 6.7 The Residual of the Kalman Filter with Model Mismatch and Non-Optimal Gain 267 6.8 Summary 274 6.9 Appendix: Estimation Error Covariance and the Kalman Gain 277 6.10 Appendix: The Role of the Ratio of Plant and the Measurement Noise Variances 279 6.11 Appendix: Orthogonal Properties of the Kalman Filter 279 6.12 Appendix: Kalman Filter Residual with Model Mismatch 285 References 287 7 System Identification 289 7.1 Overview 289 7.2 System Model 291 7.3 Kalman Filter-Based Identification Model Structure 297 7.4 LeastSquares Method 307 7.5 HighOrder LeastSquares Method 318 7.6 The Prediction Error Method 327 7.7 Comparison of High-Order Least-Squares and the Prediction Error Methods 330 7.8 Subspace Identification Method 334 7.9 Summary 340 7.10 Appendix: Performance of the Least-Squares Approach 347 7.11 Appendix: Frequency-Weighted Model Order Reduction 352 References 354 8 Closed Loop Identification 357 8.1 Overview 357 8.2 ClosedLoop System 359 8.3 Model